# Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

Authors: Changchang Lu; Sijie Chen
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation profile. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can offer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm’s error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side real-time information, which benefits VPP’s stable scheduling with a welcoming environment and transparent information. It also enhances the system’s data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP.


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## Body

## 1. Introduction

The development of all spheres of life must rely on the robust support of the power system given the accelerating advancement of science and technology. Countries all over the world are severely constrained in their ability to develop further by global energy shortages and environmental degradation. The best way to deal with this issue is to actively develop and use DER, which has the properties of low-carbon cleaning, recycling, diversification, and dispersion [1]. Power plants, substations, transmission and distribution networks, and users make up the power system, a sizable system of unified dispatching and operation. Power system automation’s primary objective is to guarantee the supply of high-quality power [2]. The operation and control of the entire power system depend heavily on the economic dispatching of the power system. It falls under the power system planning and the operation dispatching category. It is a typical optimization problem, where the objective is to efficiently use available resources while minimising system operation costs under the constraints of ensuring system load constraints and safe and stable operation [3]. In order to aggregate distributed generation resources and establish virtual power resource transactions, the energy Internet’s VPP is a crucial branch. Large-scale and widely dispersed DER can be gathered by the VPP, and its parameters can be integrated into a single external operation profile [4]. DER can freely and flexibly connect to the distribution network while being managed scientifically by the VPP, supplying the system with high-reliability, high-quality, and high-safety power services.Each component of the VPP is connected to the control centre, and data are transmitted in both directions through the smart grid. Power flow at the machine end, load at the load end, and the energy storage system are uniformly dispatched to achieve the goals of lowering power generation loss and peak grid load, optimising resource utilisation, lowering greenhouse gas emissions, and enhancing the reliability of the power supply. A consensus mechanism, encryption algorithm, distributed data storage, and point-to-point transmission are all examples of computer technologies that have been integrated and innovated to create blockchain [5]. Information security issues like trusted data transmission in the VPP can now be solved, thanks to blockchain technology’s decentralisation, transparency, automation of contract execution, traceability, and other features. The conventional distributed energy system is in an off-design operation state for a considerable amount of time as a result of the dispersed geographic locations of each DER in the VPP and the fluctuation of new energy generation and consumption. The system performance will significantly deteriorate if the current level of technical capability and management tools is used. The smooth power supply and grid-connected operation of the VPP can be achieved more easily with the efficient cooperation of DER [6]. In order to achieve a distributed VPP and achieve a reasonable load distribution among units, this paper proposes to optimise the neural network by combining the PSO algorithm. This is based on the distributed VPP’s consistency with blockchain in terms of decentralisation, point-to-point interaction, and decentralised coordination.The characteristics of transparency and fairness of blockchain provide a new application scheme to solve the problems of aggregation control and opaque transaction of decentralised resources in the VPP [7]. Based on the limited communication mechanism, distributed dispatching realizes the confidentiality of information to a great extent and enables the DER holder to actively participate in the daily trading and operation of the electricity market without disclosing important information. Traditional economic dispatching solutions mostly use centralised methods, but the increasing penetration rate of DER in the power grid poses more challenges to the centralised dispatching. In the traditional centralised dispatching, the dispatching centre or the central coordinator needs to obtain the operation information of each unit through “point-to-point” communication [8]. In this paper, the internal DER coordination problem of the VPP is analyzed under the ubiquitous power of Internet of things background; a decentralised VPP scheduling and control model is proposed, and blockchain technology is used as the cooperative control means among DER. Its main innovations and contributions are as follows:(1)
In order to increase the parallel processing ability of the network, a certain number of neural networks are trained in parallel by a certain number of subsample sets, respectively, and the training results are optimised by the PSO; finally, an optimal clustering neural network is obtained.(2)
In order to satisfy the requirements for a dependable power supply and power quality, this paper will optimise the efficiency of the operation of the power system. The algorithm’s efficacy is confirmed through simulation tests, and distributed scheduling is accomplished using blockchain, which offers a workable reference plan for a decentralised VPP operation mode.


## 2. Related Work

A system called VPP integrates different kinds of power sources to deliver dependable overall power. In comparison to traditional power plants, VPPs have the benefits of flexibility, a high rate of new energy utilisation, and a high rate of self-absorption. For instance, they can quickly produce electricity to meet the peak demand. The VPP offers greater flexibility and efficiency while partially replacing conventional power plants. The system can respond to changes better if it is more flexible, but due to its complexity, this comes with complex optimization, control, and secure communication requirements.Chen et al. put forward a collaborative dispatching model of wind, water, and wind VPP based on the classic scene set and transformed the random optimization problem into a classic scene [9]. Saad et al. compare several collaborative optimization algorithms of the VPP and simulate them with MATLAB [10]. Zhang et al. emphasized the importance of the VPP in strengthening distributed generation technology, and based on renewable energy, its value is amplified in the electricity market [11]. Al-Saedi et al. use the dynamic weight method to aggregate multiobjective into a series of single-objective optimization problems and then solve them separately [12]. Song et al. introduced the idea of the multiobjective evolutionary algorithm based on decomposition into the PSO. After aggregating multiple objectives into a single objective, the adjacent optimization problems learn from each other, thus reducing the computational cost [13]. Ameli et al. also used PSO to optimise different targets, respectively, and finally integrated them [14]. Wang et al. believe that blockchain technology is one of the most effective ways to communicate between the VPP and microgrid in the future [15]. Petersen et al. analyze the combination of the VPP and blockchain from five dimensions of sci & tech, economy, society, environmental protection, and academics [16]. Xu et al. research shows that the VPP can protect the power system by controlling the process of demand response and participating in the same, and by means of energy storage system, so as to maintain the smooth operation of the power grid [17]. Guo et al. gave the target order, dynamically determined the nearest neighbors on the first-dimension target, and determined the best among the nearest neighbors on the second dimension target, and used the neighborhood best instead of the global best, so that each particle flew to a different best, thus obtaining multiple optimal solutions [18]. Sakamuri et al. introduced the max-min function to compare the target vectors, which not only provided the dominant information between solutions but also contained diverse information [19]. Nappu and Arief improve the visibility of DER by providing interfaces between system components and use the optimal power flow algorithm to describe the VPP [20].The results of the calculations made using this model will differ significantly from the actual situation, because in actual production, placing too much emphasis on one index will cause other indexes to be ignored or even weakened. This paper suggests a PSO-NN to implement distributed scheduling of the VPP based on the consistency of a decentralised VPP and blockchain in decentralisation, point-to-point interaction, and decentralised coordination.


## 3. Methodology

### 3.1. Collaborative Control Technology of the VPP

In VPP collaboration, DER exchanges energy under the guidance of VPP operators or through competitive bidding, and operators generally enhance their ability to restrain DER through restrictions on electricity prices, subsidies, and line power [21]. The issue of optimal power dispatching in the power system has risen to the fore as a result of the ongoing growth in the size of the power system and the increasingly intricate structure of the power grid. Reactive power flow in the network and network impedance parameters both play a role in the loss of active power. The study of the best load-allocation strategy among units is at the technical heart of the VPP dispatching management, and there are many different kinds of DER units. The nonconvex operating conditions of thermal power units, such as the valve point loading effect, forbidden operating area, and multifuel option, should be taken into account when actually dispatching power, in addition to the operating characteristics of renewable energy sources and energy storage systems.In distributed architecture, agents are independent, completely equal, and have no logical master-slave relationship. According to the predefined agreement, we can determine our respective tasks and coordinate our respective behaviours and activities according to the system’s goal and state, as well as our own state, ability, and information. The communication mode between agents is shown in Figure1.Figure 1
communication method between agents.Because the DERs in the VPP do not trust one another and come from different energy subjects, such as new energy power plants, traditional energy power plants, and energy storage and load, they are unable to verify the validity and legality of the information exchanged. A consensus mechanism, an encryption algorithm, distributed data storage, and point-to-point transmission are all examples of computer technologies that have been integrated and innovated into blockchain. Simply put, blockchain is a technical system that uses cryptography, decentralisation, and de-trust to allow any number of nodes to jointly maintain a trustworthy database. The blockchain’s data structure affects the information composition of the successor node and enables the predecessor node to track the information of each block in the chain. The reactive power balance in each area can be roughly maintained by adjusting the ratio of reactive power sources and transformer branches acting as tie lines in each area. By doing this, the flow of reactive power between power grids with various voltage levels in various areas can be decreased, serving the goal of limiting the large-scale flow of reactive power in the power grid.Assuming that the generator set cost function in DER is quadratic and the cost function is represented byFiPi, the minimum power generation cost to achieve VPP is as follows:(1)minF=min∑i=1nFiPi,FiPi=aiPi2+biPi+ci,where n represents the number of DER units in the VPP, and Pi represents the output power of the unit i. The total cost of the VPP is recorded as F. ai, bi, and ci represent the coefficients of the cost function.During operation, every DER unit in the VPP satisfies the active power balance of the entire system.(2)∑i=0nPi=PLD.Among them,PLD represents the total load demand of all users. The load is distributed among the units according to the “equal consumption microincrease rate criterion.” When the optimal operation is achieved, the microincrement characteristics λ of all the units are consistent, and λ can be calculated by the first-order differential, namely,(3)λ=dFidPi=2aiPi+bi.Therefore,λ can be used as a consistency variable between nodes in blockchain, and it can be adjusted as the load changes, but the entire network remains consistent.In the blockchain system, intelligent contracts ensure that both parties’ rights and obligations as well as the determination of the contract’s execution are upheld. Once the requirements are satisfied, the transaction will be carried out automatically without artificial promotion or oversight by a third party, which greatly increases the efficiency of the transaction execution. Distributed energy and power sources will continue to proliferate, and transactions involving the VPP and related power generation resources will become more frequent. The adoption of adaptive and decentralised energy scheduling will increase, and its isomorphic blockchain will give distributed energy sources a solid and reliable foundation for data interaction. A strong theoretical and practical foundation is provided for the architecture design of a distributed energy system based on blockchain by the decentralised cooperation mode of a power system based on the multiagent consistency theory and current mainstream blockchain technology. The blockchain consensus mechanism enables effective distributed communication between units, and data broadcasting and information interaction are used to ensure the consistency of each unit’s operational characteristics. It is therefore possible to implement the blockchain consensus mechanism to achieve the distributed dispatching strategy and the best VPP performance. Through the aforementioned master node selection algorithm, the power supply node and the power consumption node choose the master node. The master node then predicts the power consumption data for the upcoming time period and sends the predicted data to the blockchain for propagation.


### 3.2. Distributed Scheduling of the VPP Based on the PSO-NN

Because the complex system contains many variables, the relationship between variables is complex and changeable, and the system scale is relatively large; there are more uncertain factors that have important influence. Multiagent technology has the characteristics of autonomy, distribution, and coordination, which can realize the self-organization, self-learning, and reasoning ability of the system. When multiagent technology is used to solve practical problems, it has robustness, reliability, and high problem-solving efficiency. The decentralised cooperation mode of the power system based on multiagent consistency theory is highly consistent with the concept of current mainstream blockchain technology, which provides a good theoretical and practical foundation for the architecture design of the distributed energy system based on blockchain.The load forecasting model in the power system makes predictions about the future load demand based on historical data and the current power supply situation. The relationship between historical data is complicated for a number of reasons, and some of the data are even wrong. Additionally, load forecasting using conventional statistical methods must be based on data from a large sample of loads. The outcomes of using traditional methods are frequently very dissimilar from the reality when dealing with the relationship between such a large amount of complex data. Few parameters, a straightforward structure, and straightforward operation are the benefits of the PSO. After improvement, it has a good ability to locate the global optimal solution and can successfully prevent the algorithm’s premature convergence. Because the PSO algorithm is better suited to solving single-objective problems, it suffers from some drawbacks when applied to multiobjective problems. These drawbacks include low solving efficiency, a high number of subjective experience factors, a significant amount of calculation, complex algorithm settings, and front-end sensitivity. The structure and operation mode of the VPP system are shown in Figure2.Figure 2
The structure and operation mode of the VPP system.The fundamental idea behind artificial neural networks [22] is to mimic how the human brain works in order to transmit data and perform intricate operations between neurons. In essence, there are two processes that make up an artificial neural network’s operation. The first is the training process. In this procedure, the neural network’s weights and partial weights are acquired through training the neural network. The simulation process is the second. The simulation of the neural network yields information about the prediction output value or accuracy of the network. PSO is a novel kind of swarm intelligence optimization algorithm that excels at parallel search, ease of implementation, simplicity, and computational efficiency. It can find the global optimal solution to the issue with a high probability and is appropriate for complex optimization problems. Particles draw lessons from the groups and their own successful information gathering experiences to inform their next course of action. The flow of the PSO-NN algorithm is shown in Figure 3.Figure 3
PSO-NN algorithm flow.M is the number of neurons in the input layer, N is the number of neurons in the hidden layer, wij is the network connection weight between neurons in the input layer and the hidden layer, and wj is the network connection weight between neurons in the hidden layer and the output layer. The expression of the implicit layer function in this paper is as follows:(4)ϕx=cos1.75xexp−x22.By scaling and translating the above formula, the wavelet basis function can be obtained as follows:(5)ϕa,bx1aψx−ba,where a and b are the scaling factor and translation factor, respectively. The output layer neuron function expression in this paper is as follows:(6)δx=11+exp−x.Its output is as follows:(7)yx=δ∑j=0Nwjψa,b∑m=0Mwijxm.The VPP participates actively in power market transactions as a significant component of the power market. There is currently no two-way information symmetry between distributed energy and the VPP, and as a result, the benefit distribution mechanism in the VPP is not currently accessible to the outside world. This results in higher credit costs and higher transaction costs when buying and selling electricity. The VPP has the characteristics of centralised nodes and requires sufficient authority to coordinate, induce, and control the grid-connected behaviour of each DER in order to ensure the safe and reliable operation of the power system. A decentralised platform is currently required to guarantee fairness. The network’s clustering results are unstable because of the sensitivity to data initialization and the difficulty of using the traditional linear connection weight function to show the subtle differences between the input and output linear transformation attribute values. Through two-way communication technology, the VPP implements the scheduling of information and data for each component, such as the power generation side, the demand side, and the electricity trading market. The operation scheduling process of the VPP incorporates a proposed energy blockchain network model, allowing DER to effectively participate in electricity market transactions. Through the cryptographic features of blockchain itself, it guarantees the VPP to obtain a higher level of information security while also increasing the overall operation efficiency of the VPP.The programmable feature of intelligent contract enables both parties to agree on various transaction terms, and it is applicable to all kinds of procedural rules and has a very broad application scenario. In the operation of the VPP, the transactions of each node can be automatically and safely executed through intelligent contracts. In the whole process of iterative optimization, the examples of failures experienced by particles can be shown as the positions of poor fitness values of particles themselves or groups of particles. Let us assume that the worst position searched by particles so far is as follows:(8)si=si1,si2,si3,…,sin.The worst position searched so far by the entire particle swarm in the iterative optimization process is as follows:(9)sg=sg1,sg2,sg3,…,sgn.Then, the velocity and position update formula of thei-th particle can be obtained, namely,(10)vidk+1=ωvidk+cirixidk−sidk+c2r2xidk−sgdk,xidk+1=xidk+vidk+1.The above formula, which states that particles only learn from unsuccessful examples, would obviously lead to an update of particles that is inconsistent with actual experience. The idea of inertia weight is added to the basic PSO in order to enhance the convergence performance and optimise the solution space. The modified version of the original PSO algorithm’s velocity and position formula is as follows:(11)vidk+1=ωvidk+c1r1pid−xidk+c2r2pgd−xid,xidk+1=xidk+vidk+1.In the formula,vi=vi1,vi2,vi3,…,vin is the speed of the particle i, which represents the distance between the current position of the particle i and the next target position; xi=xi1,xi2,xi3,…,xin is the current position of the particle i; pi is the individual optimal solution searched by the particle so far; pg is the optimal solution searched by the entire particle swarm so far. ω is the inertia weight. Due to the sensible selection of the inertia weight, the particle has a balanced capacity for exploration and development. The inertia weight value is used to characterise the size of the particle’s current speed inheritance.The number of the two subpopulations is constantly changing, so that each particle can get a lot of learning information from its own experience and the group’s experience. After every certain iteration, the particles in the population are adjusted according to the proportional coefficient, and the whole population is re-formed into two new subpopulations [23]. When the optimization is in the late stage, the particles are concentrated near the optimal value. At this time, the number of particles that learn from the failure experience will be far less than the number of particles that get information from the success experience. Then, the particles continue to iterate and update until all the particles in the population adopt the learning strategy of finding the optimal value to iterate. When evaluating the quality of particles, the fitness and concentration of particles should be taken as the standard. If the fitness of particles is better and the concentration of particles is lower, then the quality of particles will be better. Therefore, the improved algorithm will suppress the particles with poor quality, and all particles are equipped with a mutation rate, and the mutation rate will change with the change of particle quality.


## 3.1. Collaborative Control Technology of the VPP

In VPP collaboration, DER exchanges energy under the guidance of VPP operators or through competitive bidding, and operators generally enhance their ability to restrain DER through restrictions on electricity prices, subsidies, and line power [21]. The issue of optimal power dispatching in the power system has risen to the fore as a result of the ongoing growth in the size of the power system and the increasingly intricate structure of the power grid. Reactive power flow in the network and network impedance parameters both play a role in the loss of active power. The study of the best load-allocation strategy among units is at the technical heart of the VPP dispatching management, and there are many different kinds of DER units. The nonconvex operating conditions of thermal power units, such as the valve point loading effect, forbidden operating area, and multifuel option, should be taken into account when actually dispatching power, in addition to the operating characteristics of renewable energy sources and energy storage systems.In distributed architecture, agents are independent, completely equal, and have no logical master-slave relationship. According to the predefined agreement, we can determine our respective tasks and coordinate our respective behaviours and activities according to the system’s goal and state, as well as our own state, ability, and information. The communication mode between agents is shown in Figure1.Figure 1
communication method between agents.Because the DERs in the VPP do not trust one another and come from different energy subjects, such as new energy power plants, traditional energy power plants, and energy storage and load, they are unable to verify the validity and legality of the information exchanged. A consensus mechanism, an encryption algorithm, distributed data storage, and point-to-point transmission are all examples of computer technologies that have been integrated and innovated into blockchain. Simply put, blockchain is a technical system that uses cryptography, decentralisation, and de-trust to allow any number of nodes to jointly maintain a trustworthy database. The blockchain’s data structure affects the information composition of the successor node and enables the predecessor node to track the information of each block in the chain. The reactive power balance in each area can be roughly maintained by adjusting the ratio of reactive power sources and transformer branches acting as tie lines in each area. By doing this, the flow of reactive power between power grids with various voltage levels in various areas can be decreased, serving the goal of limiting the large-scale flow of reactive power in the power grid.Assuming that the generator set cost function in DER is quadratic and the cost function is represented byFiPi, the minimum power generation cost to achieve VPP is as follows:(1)minF=min∑i=1nFiPi,FiPi=aiPi2+biPi+ci,where n represents the number of DER units in the VPP, and Pi represents the output power of the unit i. The total cost of the VPP is recorded as F. ai, bi, and ci represent the coefficients of the cost function.During operation, every DER unit in the VPP satisfies the active power balance of the entire system.(2)∑i=0nPi=PLD.Among them,PLD represents the total load demand of all users. The load is distributed among the units according to the “equal consumption microincrease rate criterion.” When the optimal operation is achieved, the microincrement characteristics λ of all the units are consistent, and λ can be calculated by the first-order differential, namely,(3)λ=dFidPi=2aiPi+bi.Therefore,λ can be used as a consistency variable between nodes in blockchain, and it can be adjusted as the load changes, but the entire network remains consistent.In the blockchain system, intelligent contracts ensure that both parties’ rights and obligations as well as the determination of the contract’s execution are upheld. Once the requirements are satisfied, the transaction will be carried out automatically without artificial promotion or oversight by a third party, which greatly increases the efficiency of the transaction execution. Distributed energy and power sources will continue to proliferate, and transactions involving the VPP and related power generation resources will become more frequent. The adoption of adaptive and decentralised energy scheduling will increase, and its isomorphic blockchain will give distributed energy sources a solid and reliable foundation for data interaction. A strong theoretical and practical foundation is provided for the architecture design of a distributed energy system based on blockchain by the decentralised cooperation mode of a power system based on the multiagent consistency theory and current mainstream blockchain technology. The blockchain consensus mechanism enables effective distributed communication between units, and data broadcasting and information interaction are used to ensure the consistency of each unit’s operational characteristics. It is therefore possible to implement the blockchain consensus mechanism to achieve the distributed dispatching strategy and the best VPP performance. Through the aforementioned master node selection algorithm, the power supply node and the power consumption node choose the master node. The master node then predicts the power consumption data for the upcoming time period and sends the predicted data to the blockchain for propagation.


## 3.2. Distributed Scheduling of the VPP Based on the PSO-NN

Because the complex system contains many variables, the relationship between variables is complex and changeable, and the system scale is relatively large; there are more uncertain factors that have important influence. Multiagent technology has the characteristics of autonomy, distribution, and coordination, which can realize the self-organization, self-learning, and reasoning ability of the system. When multiagent technology is used to solve practical problems, it has robustness, reliability, and high problem-solving efficiency. The decentralised cooperation mode of the power system based on multiagent consistency theory is highly consistent with the concept of current mainstream blockchain technology, which provides a good theoretical and practical foundation for the architecture design of the distributed energy system based on blockchain.The load forecasting model in the power system makes predictions about the future load demand based on historical data and the current power supply situation. The relationship between historical data is complicated for a number of reasons, and some of the data are even wrong. Additionally, load forecasting using conventional statistical methods must be based on data from a large sample of loads. The outcomes of using traditional methods are frequently very dissimilar from the reality when dealing with the relationship between such a large amount of complex data. Few parameters, a straightforward structure, and straightforward operation are the benefits of the PSO. After improvement, it has a good ability to locate the global optimal solution and can successfully prevent the algorithm’s premature convergence. Because the PSO algorithm is better suited to solving single-objective problems, it suffers from some drawbacks when applied to multiobjective problems. These drawbacks include low solving efficiency, a high number of subjective experience factors, a significant amount of calculation, complex algorithm settings, and front-end sensitivity. The structure and operation mode of the VPP system are shown in Figure2.Figure 2
The structure and operation mode of the VPP system.The fundamental idea behind artificial neural networks [22] is to mimic how the human brain works in order to transmit data and perform intricate operations between neurons. In essence, there are two processes that make up an artificial neural network’s operation. The first is the training process. In this procedure, the neural network’s weights and partial weights are acquired through training the neural network. The simulation process is the second. The simulation of the neural network yields information about the prediction output value or accuracy of the network. PSO is a novel kind of swarm intelligence optimization algorithm that excels at parallel search, ease of implementation, simplicity, and computational efficiency. It can find the global optimal solution to the issue with a high probability and is appropriate for complex optimization problems. Particles draw lessons from the groups and their own successful information gathering experiences to inform their next course of action. The flow of the PSO-NN algorithm is shown in Figure 3.Figure 3
PSO-NN algorithm flow.M is the number of neurons in the input layer, N is the number of neurons in the hidden layer, wij is the network connection weight between neurons in the input layer and the hidden layer, and wj is the network connection weight between neurons in the hidden layer and the output layer. The expression of the implicit layer function in this paper is as follows:(4)ϕx=cos1.75xexp−x22.By scaling and translating the above formula, the wavelet basis function can be obtained as follows:(5)ϕa,bx1aψx−ba,where a and b are the scaling factor and translation factor, respectively. The output layer neuron function expression in this paper is as follows:(6)δx=11+exp−x.Its output is as follows:(7)yx=δ∑j=0Nwjψa,b∑m=0Mwijxm.The VPP participates actively in power market transactions as a significant component of the power market. There is currently no two-way information symmetry between distributed energy and the VPP, and as a result, the benefit distribution mechanism in the VPP is not currently accessible to the outside world. This results in higher credit costs and higher transaction costs when buying and selling electricity. The VPP has the characteristics of centralised nodes and requires sufficient authority to coordinate, induce, and control the grid-connected behaviour of each DER in order to ensure the safe and reliable operation of the power system. A decentralised platform is currently required to guarantee fairness. The network’s clustering results are unstable because of the sensitivity to data initialization and the difficulty of using the traditional linear connection weight function to show the subtle differences between the input and output linear transformation attribute values. Through two-way communication technology, the VPP implements the scheduling of information and data for each component, such as the power generation side, the demand side, and the electricity trading market. The operation scheduling process of the VPP incorporates a proposed energy blockchain network model, allowing DER to effectively participate in electricity market transactions. Through the cryptographic features of blockchain itself, it guarantees the VPP to obtain a higher level of information security while also increasing the overall operation efficiency of the VPP.The programmable feature of intelligent contract enables both parties to agree on various transaction terms, and it is applicable to all kinds of procedural rules and has a very broad application scenario. In the operation of the VPP, the transactions of each node can be automatically and safely executed through intelligent contracts. In the whole process of iterative optimization, the examples of failures experienced by particles can be shown as the positions of poor fitness values of particles themselves or groups of particles. Let us assume that the worst position searched by particles so far is as follows:(8)si=si1,si2,si3,…,sin.The worst position searched so far by the entire particle swarm in the iterative optimization process is as follows:(9)sg=sg1,sg2,sg3,…,sgn.Then, the velocity and position update formula of thei-th particle can be obtained, namely,(10)vidk+1=ωvidk+cirixidk−sidk+c2r2xidk−sgdk,xidk+1=xidk+vidk+1.The above formula, which states that particles only learn from unsuccessful examples, would obviously lead to an update of particles that is inconsistent with actual experience. The idea of inertia weight is added to the basic PSO in order to enhance the convergence performance and optimise the solution space. The modified version of the original PSO algorithm’s velocity and position formula is as follows:(11)vidk+1=ωvidk+c1r1pid−xidk+c2r2pgd−xid,xidk+1=xidk+vidk+1.In the formula,vi=vi1,vi2,vi3,…,vin is the speed of the particle i, which represents the distance between the current position of the particle i and the next target position; xi=xi1,xi2,xi3,…,xin is the current position of the particle i; pi is the individual optimal solution searched by the particle so far; pg is the optimal solution searched by the entire particle swarm so far. ω is the inertia weight. Due to the sensible selection of the inertia weight, the particle has a balanced capacity for exploration and development. The inertia weight value is used to characterise the size of the particle’s current speed inheritance.The number of the two subpopulations is constantly changing, so that each particle can get a lot of learning information from its own experience and the group’s experience. After every certain iteration, the particles in the population are adjusted according to the proportional coefficient, and the whole population is re-formed into two new subpopulations [23]. When the optimization is in the late stage, the particles are concentrated near the optimal value. At this time, the number of particles that learn from the failure experience will be far less than the number of particles that get information from the success experience. Then, the particles continue to iterate and update until all the particles in the population adopt the learning strategy of finding the optimal value to iterate. When evaluating the quality of particles, the fitness and concentration of particles should be taken as the standard. If the fitness of particles is better and the concentration of particles is lower, then the quality of particles will be better. Therefore, the improved algorithm will suppress the particles with poor quality, and all particles are equipped with a mutation rate, and the mutation rate will change with the change of particle quality.


## 4. Result Analysis and Discussion

The pollution discharge can be significantly improved by readjusting and combining the output of different fuel units through dispatching methods, but the cost may go up. The reason is that low-carbon energy units, such as the liquefied natural gas and low-sulfur fuel, will produce more output than other units when the dispatching process is oriented to reduce the pollution discharge, increasing the cost of power generation. The fundamental requirement of the entire power system is to pursue the maximum economic benefit of the system, which is predicated on ensuring the safe and dependable operation of the system. High production, transmission, distribution, and consumption efficiencies are all referred to as aspects of the electric power system’s economy. Even though there are numerous factors that affect the cost of power generation, developing a model is difficult, but it can accurately represent the distribution of output among generators across the entire power system. The simulation is performed through experiments to confirm the efficacy of the algorithm suggested in this paper. For the unit’s operating conditions and starting power, see Table1.Table 1
Operating parameters of the unit.
GeneratorCost coefficientGenerating power of unitMGG 11.22535MGG 21.12140MGG 31.33165MGGs are connected through a blockchain network, and each MGG is a node in the network. In the presence of network delay, we test the change of consistency variables.Figure4 demonstrates that the system is not functioning at its best because at the initial time t = 1, the consistency variables of each unit are different, which does not satisfy the requirement of an equal consumption increment rate. The system reaches its best operational state at time t = 6.6, when the consistency variables are consistent. Figure 5 compares the total power consumed by the load and the total power produced by the unit during the consensus-building process.Figure 4
Changes of microincrement eigenvalues.Figure 5
Comparison of the total load power and total power generation.From Figure5, we can find the system fluctuation caused by network delay and consensus calculation, but the power balance is finally achieved. Figure 6 shows the active power adjustment of each MGG unit, and the final power value is stable in the optimal operation state.Figure 6
Power variation of each unit.In order to effectively coordinate the regional electricity demand with the electricity demand of the electricity wholesale market, VPP technology can recognise the possibility that a household or individual load will feed back excess electricity to the power grid. It can also allocate the working hours of periodic distributed generation and distributable distributed generation in a reasonable manner. The blockchain system has strong robustness and reliability in data storage because there is no centralised central control, all nodes can back up the information in blockchain partially or completely, and the data loss of any node will not affect the system’s normal operation. A specific incentive mechanism is used to make sure that every node in the distributed system takes part in the information exchange process in the blockchain system, which is operated and maintained by every node in the network. Enterprises give each objective varying levels of importance when making multiobjective decisions. Businesses can sort different objectives first for better decision-making, prioritise important objectives when choosing schemes, eliminate those that cannot meet important objectives, prioritise secondary objectives when choosing schemes, and choose a few schemes before choosing the best one.The difference between the actual output and the expected output of power prediction based on the PSO-NN is shown in Figure7. The corresponding predicted performance indicators are shown in Table 2. The comparison between the short-term power forecast result based on the PSO-NN and the actual value is shown in Figure 8.Figure 7
Electricity prediction error based on the PSO-NN.Table 2
Comparison of evaluation indicators.
Average absolute errorMean square errorAverage absolute percent errorMean square percentage errorPSO-NN3.51022.3320.0760.018BPNN2.99113.2740.0620.008Figure 8
Electric power prediction simulation based on the PSO-NN.The prediction error and prediction result graphs show that when compared to the prediction error and prediction graph of the BP neural network, the prediction error of the PSO-NN is smaller, and its prediction effect is better than that of the BP neural network. The prediction accuracy rate of the PSO-NN can reach 94.98 percent. The mean square error of the prediction effect is 13.27, and the average absolute error, mean square error, mean absolute percentage error, and mean square percentage error are all less than those predicted by the BP neural network, according to the evaluation index of prediction performance. Supplying electricity to units with high electric energy utilisation efficiency and low pollution emission as much as possible can effectively improve the electricity utilisation efficiency, protect the environment and protect energy, and achieve the effect of conservation under the conditions of the electricity market, according to the indicators of electric energy utilisation efficiency, pollution emission, and industrial production capacity of each electricity consuming unit.


## 5. Conclusion

The primary research focus of this paper is on how to make the power system meet all operational stability indicators while also minimising power generation costs, reducing environmental pollution, and minimising waste discharge. The traditional VPP has some drawbacks, including centralised control, mistrust between distributed energy agents, and simple information transmission modification. Blockchain technology’s decentralised, anonymous, and transparent transmission can easily fix these issues. The PSO algorithm is used in this paper to optimise the neural network, achieve the distributed VPP dispatching, and achieve a fair load distribution among the units. The PSO algorithm is prone to local convergence and finds the global optimal value with difficulty because once the local optimal value is discovered during the optimization process, other particles will quickly move toward it. According to the simulation results, this algorithm’s error is negligible and its accuracy can reach 94.98 percent. In addition to effectively resolving the issues of mutual mistrust and high information exchange costs among the key players in the current VPP, this model can more accurately reflect the real-time information of the demand side. To solve the problem of achieving a more reliable and efficient consensus in the high error rate environment is the focus of the next stage. For the neural network algorithm of PSO, the next step can be to divide the particle swarm into two subpopulations and optimise the subpopulations, respectively.



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*Source: 3222249-2022-09-13.xml*