# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 1 Introduction
The same concepts have been used in [20] to design an optimal, autonomous, and distributed bidding-based energy optimization scheduling algorithm to maximize profit and energy balancing efficiency of H-MGs under residential loads. A comprehensive simulation study was carried out to reveal the effectiveness of the proposed method in lowering the market clearing price, increasing H-MG responsive load consumption, and promoting local generation. Optimal management system of battery energy storage is proposed in [21] to enhance the resilience of a PV-based commercial building while maintaining its operational cost at a minimum level. The methodology is based on linear optimization programming problem with Conditional Value at Risk (CVaR) incorporated in the objective function. CVaR is used to account for the uncertainty in the intermittent PV system generated power and that in the electricity price. MATLAB simulation studies were carried out to evaluate the performance of the proposed method. There are few studies in existing literature addressing the experimental validation of distributed control algorithms. Experimental evaluations of real deployments are thus lacking. In [22], a gossip-based P2P voltage control has been tested in a pilot site, the work is part of the European Commission FP7 DREAM project. Six households were equipped with smart control agents, which measure the households' consumption and control the households' flexible loads. Each agent is connected to a local Wi-Fi router (internet gateway) and a virtual private network is then used to enable P2P communication between the neighboring agents. In [23], a multi-agent platform has been implemented and used to test a dual-decomposition-based optimization method for controlling the prosumers' flexibility. The distributed agents are implemented in Raspberry Pi computers. The agent-based control algorithm of each agent is implemented in Python and executed via Matlab calls. The setup is part of Local Intelligent Networks and Energy Active Region (LINEAR) project [24]. In [25], a gossiping P2P semantic overlay network is implemented by a toolbox, Agora+, enabling P2P communication between agents. The toolbox has been used to implement a distributed tertiary control algorithm, which allows groups of generators to operate at an economical optimum. In [26], distributed reactive power control has been implemented and tested using real power inverters. Each inverter is considered to be an agent where coordination between the agents is obtained by exchanging information via an IP-based communication network. This paper discusses the results of the experimental validation of a dual-decomposition-based P2P voltage control algorithm developed within the P2P-SmarTest project. A simulation already demonstrated the effectiveness of this algorithm [14] and this paper demonstrates it experimentally. The voltage control problem is formulated as an optimization problem. The proposed method calculates the minimum change in reactive power and active power needed to maintain the voltages within the limits. The dual-decomposition method decomposes an optimization problem (with separable cost functions and coupled constraints) into sub-problems, suitable for distributed control. Dual-decomposition applies the theory of Lagrangian multipliers and duality to convert a centralized constraint optimization problem into a fully distributed constraint optimization problem. The proposed dual-decomposition method differs from the classical dual-decomposition. In classical dual-decomposition [27], there is a need for a central agent to calculate the Lagrangian multipliers (control signals), whereas in the proposed dual-decomposition method, the Lagrangian multipliers are calculated locally and each agent communicates its Lagrangian multipliers to the other agents in a P2P fashion. Our main contributions can be summarized as follows.