# Blockchain Solution for Buildings' Multi-Energy Flexibility Trading Using Multi-Token Standards
## 2 Related Work  
Buildings are considered as central elements to the success of the energy transition towards an integrated renewable energy system in Europe [1]. Their flexibility is a key asset in achieving a reliable, decarbonized, and cost-effective energy system [2]. The literature study was organized into three categories: (i) buildings' flexibility within an integrated multi-energy system, (ii) blockchain and tokenization solutions for peer-to-peer energy trading of buildings, and finally (iii) the use of tokenization in cross-energy sectors. In the first category, a few approaches in the literature deal with the potential contribution of buildings' heat and electricity flexibility to combined energy system management. In [15], the authors propose the concept of integrated demand response (DR), where residential consumers can use their flexibility not only by reducing off-peak electricity consumption, but also by converting the electricity to heat or gas. [PERSON] et al. argue that demand response for district heating has great potential [16] and investigate optimal strategies from the district heating operator point. They conclude that such strategies have a slight positive effect on costs and emissions at the district level of buildings if heat storage such as hot water tanks is not available. [PERSON] et al. analyze the potential of district heating, targeting swimming pools and halls, which pose significant heat demands and larger heat storage capacity [17]. The authors create a simulation model and propose a rule-based algorithm that uses the dynamic district heat price as an optimization factor to take advantage of  
Figure 1: Building energy flexibility trading using our blockchain and ERC-1155 solution. buildings in heat-oriented DR services. In [18], the authors study and evaluate a demand response model for central heating systems of apartment buildings. It considers weather forecasts, indoor temperatures, and decreases in space heating temperatures when the demand for domestic hot water is at a high level. The overall objective is to cut or shift the peaks in gas consumption, thus levelling the heat demand to a flatter profile. [PERSON] et al. analyze the energy- and cost-saving potential of demand response in district heating networks from Germany and Finland [19]. A real-time pricing-based strategy is selected to calculate the hourly indoor temperature set points, reporting energy savings of between 7 and 8%. In [20], the integration of a predictive DR control for residential buildings that feature heat pumps and on-site energy generation is proposed. The prediction process outputs the heating demand for the next 24 h and is implemented by means of a hybrid wavelet transformation and a dynamic neural network. Similarly, an integrated model for optimal scheduling of cooling, heating, power, gas, and water sources in a multi-energy microgrid is proposed in [21]. Demand-side services are proposed to allow the building and operators to participate in the power, heat, and gas markets using energy conversion facilities. [PERSON] et al. focus on the problem of the imbalance between supply and demand and propose a model of integrated electrical, heating, natural gas, and water distribution systems for participating in demand response programs [22]. A price-based strategy is formulated for the electrical, gas, and water loads that benefits from a two-stage robust optimization technique to achieve optimal scheduling of resources. The multi-energy microgrid (heat-power-gas) management problem is tackled by developing models for converting energy carriers and changing the energy consumption pattern for a given period [23]. A thorough review of the potential power-to-heat DR for multi-energy networks is presented in [24], arguing that most of the research should focus on energy markets and power-to-heat demand response, requiring the refinement of economic and policy frameworks.