Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost recovery.With the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent years.Thus,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential practices.This paper can provide a useful reference and insights for the activation of demand-side flexibility potential.Hopefully,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets

It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

Reinforcement learning-driven local transactive energy market fordistributed energy resources

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energyfrom distributed resources. In combination with automation, they promise to provide an effective energymanagement framework that is fair and brings system-level savings. The cooperative–competitive natureof energy markets calls for multi-agent based automation with learning energy trading agents. However,depending on the dynamics of the agent–environment interaction, this approach may yield unintended behaviorof market participants. Thus, the design of market mechanisms suitable for reinforcement learning agentsmust take into account this interplay. This article introduces autonomous local energy exchange (ALEX) asan experimental framework that combines multi-agent learning and double auction mechanism. Participantsdetermine their internal price signals and make energy management decisions through market interactions,rather than relying on predetermined external price signals. The main contribution of this article is examinationof compatibility between specific market elements and independent learning agents. Effects of different marketproperties are evaluated through simulation experiments, and the results are used for determine a suitablemarket design. The results show that market truthfulness maintains demand-response functionality, while weakbudget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavioris compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsiveto fluctuations in the community net load compared to the time-of-use. The more accurate accounting ofrenewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability tobetter facilitate demand response.

A Grid-friendly Neighborhood Energy Trading Mechanism

More customers are tending to install batteries with photovoltaic(PV), so they can better control their electricity bills. In this context, customers may be tempted to go offgrid at a substantial up-front cost, leading electricity companies into a death spiral, thereby raising electricity price further on those remaining on grid. Neighborhood energy markets can promote the sharing of locally generated renewable energy and encourage prosumers to stay on grid with financial incentives. A novel neighborhood energy trading(NET) mechanism is developed using the topology of existing radial distribution network to encourage sustainable energy sharing in neighborhood and encourage prosumers to stay on grid. This mechanism considers loss, congestion management, and voltage regulation, and it is scalable with low computation and communication overhead.An IEEE test system is used to validate the NET mechanism.The simulation shows that the price and flow results are obtained with fast computation speed(within 10 iterations) and with loss reflected, flow limit reinforced, and voltage regulated.This study proves that the economic demand-supply-based pricing mechanism can be applied effectively in distribution networks to help encourage more renewable energy sharing in sustainable neighborhood and avoid energy network death spiral.