A game for win–win strategy of electricity-carbon P2P trading with carbon certification price

Peer-to-peer trading price and strategy optimization considering different electricity market types, tariff systems, and pricing models

This paper proposes a three-stage multi-energy sharing strategy for a gas-electricity integrated energy system (IES). It aims to solve the multi-energy imbalance problem among energy hubs (EHs) based on the peer-to-peer (P2P) trading mode. First, considering the characteristics of multi-energy coupling and conversion, the quantity of shareable energy is determined for EHs that participate in the P2P trading. Furthermore, EHs conduct multi-bilateral negotiations based on the Raiffa-Kalai-Smorodinsky bargaining solution (RBS) to determine the optimal energy trading price. Finally, the buyer agent and the seller agent will design the optimal energy sharing trading strategy for all EHs. Moreover, the results show that the pricing mechanism improves the fairness of satisfaction obtained by the EHs from the utility distribution, and the social welfare of the system is improved, which proves that the three-stage multi-energy sharing trading strategy is effective.

Optimization of social welfare in P2P community microgrid with efficient decentralized energy management and communication-efficient power trading

Optimization strategies for green power and certificate trading in China considering seasonality: An evolutionary game-based system dynamics

Peer-to-peer electricity trading in grid-connected residential communities with household distributed photovoltaic

With a large amount of distributed energy resources connected to the distribution network, problems emerge for distributed energy, such as a lack of timely information interaction and difficulty in scheduling. This paper employs the virtual power plant (VPP) concept for resource coordination and control. To study the trading between VPP, this paper proposes a Pear to Pear (P2P) trading mode. First, the VPP aggregation model is established, including photovoltaic (PV), energy storage, central air-conditioning system, gas turbines, and user loads. Second, considering the influence of PV output uncertainty on the trading strategy, the scheduling model based on the stochastic optimization method is built. Then, Shapley value method is used to allocate multiple VPP cooperation surpluses. Finally, simulation results are used to verify the effectiveness of the proposed method by the comparison with traditional centralized trading form, P2P trading of multi-VPP could better encourage VPP to participate in energy sharing and increase social benefits.

This research addresses a demand side management (DSM) system coordinated with Peer-to-Peer (P2P) energy trading among the households in the smart grid. It considers the components which have significant impact on cost optimization, e.g., storage, renewables, and microgrid. The model utilizes load and source scheduling, and energy trading strategies for cost optimization. It also addresses the inconvenience created to the users by delaying certain tasks. The contributions of the research are threefold. First, to our knowledge, this is the first optimal model which integrates DSM with P2P energy trading. The solutions of the proposed model determine optimal microgrid energy and price for P2P trading, which was not considered previously. Second, P2P energy trading in the microgrid potentially results in an unfair cost distribution among the participating households. We address this unfair cost distribution problem by employing Pareto optimality, ensuring that no households will be worse off to improve the cost of others. Third, our proposed trading strategy considers total cost optimization in a microgrid. The model utilizes all available energy to minimize energy cost. Therefore, there is a very low risk of energy waste, which is typically neglected in other energy trading strategies.

Simulating price patterns for tradable green certificates to promote electricity generation from wind

A green energy certificate transaction, as one of the ways to meet China’s renewable energy quota system, is a virtual transaction through the renewable energy trading system. Different from the market mechanism of only one transaction in the past audit cycle, this paper establishes an agent-based renewable energy system with multi-period trading according to the existing green energy certificate trading policy. With simulation of a period of audit of green spot multi-period transactions, the agent of limited rationality makes it in the pursuit of their utility under the premise of interaction. The experiment observes the utility level of agents under different trading strategies, verifies the influence of different policy behaviors on the market, and obtains the market equilibrium state. The conclusion shows that there is a serious green certificate premium in the early stage of the market. After adjustment by fines, the green certificate price of 0.05-0.08 yuan can maximize social welfare and stimulate the enthusiasm for individual purchases.

P2P power trading of nanogrids for power management in consideration of battery lifetime of ESS

This paper presents a novel framework for Peer-to-Peer (P2P) energy trading that integrates uncertainty-aware prediction with multi-agent reinforcement learning (MARL), addressing a critical gap in current literature. In contrast to previous works relying on deterministic forecasts, the proposed approach employs a heteroscedastic probabilistic transformer-based prediction model called Knowledge Transformer with Uncertainty (KTU) to explicitly quantify prediction uncertainty, which is essential for robust decision-making in the stochastic environment of P2P energy trading. The KTU model leverages domain-specific features and is trained with a custom loss function that ensures reliable probabilistic forecasts and confidence intervals for each prediction. Integrating these uncertainty-aware forecasts into the MARL framework enables agents to optimize trading strategies with a clear understanding of risk and variability. Experimental results show that the uncertainty-aware Deep Q-Network (DQN) reduces energy purchase costs by up to 5.7% without P2P trading and 3.2% with P2P trading, while increasing electricity sales revenue by 6.4% and 44.7%, respectively. Additionally, peak hour grid demand is reduced by 38.8% without P2P and 45.6% with P2P. These improvements are even more pronounced when P2P trading is enabled, highlighting the synergy between advanced forecasting and market mechanisms for resilient, economically efficient energy communities.

This paper proposes two novel strategies for determining the bilateral trading preferences of households participating in a fully Peer-to-Peer (P2P) local energy market. The first strategy matches between surplus power supply and demand of participants, while the second is based on the distance between them in the network. The impact of bilateral trading preferences on the price and amount of energy traded is assessed for the two strategies. A decentralized fully P2P energy trading market is developed to generate the results in a day-ahead setting. After that, a permissioned blockchain-smart contract platform is used for the implementation of the decentralized P2P trading market on a digital platform. Actual data from a residential neighborhood in the Netherlands, with different varieties of distributed energy resources, is used for the simulations. Results show that in the two strategies, the energy procurement cost and grid interaction of all participants in P2P trading are reduced compared to a baseline scenario. The total amount of P2P energy traded is found to be higher when the trading preferences are based on distance, which could also be considered as a proxy for energy efficiency in the network by encouraging P2P trading among nearby households. However, the P2P trading prices in this strategy are found to be lower. Further, a comparison is made between two scenarios: with and without electric heating in households. Although the electrification of heating reduces the total amount of P2P energy trading, its impact on the trading prices is found to be limited.

With more distributed energy resources penetrated into the residential community, nanogrid based peer-to-peer (P2P) energy market has rapidly emerged over recent years. Due to the complexities on the decision-making process of each market participant, an efficient, fair and beneficial oriented bidding strategy is thus necessary. In this article, a two-stage bidding strategy for P2P trading of nanogrid is proposed. To overcome the limitations of traditional methods, in the first stage, a supply-demand relationship considered two-step price predictor, which aims to promote the usage of local renewable energy, is formulated to provide the guidance on transaction adjustment. In the second stage, trading preference based simultaneous game-theoretic approach is fully introduced, which can optimize the market equilibrium and then increase the social welfare of the P2P market. Additionally, to mitigate the possible failure of price matching, value-at-risk is implemented through the trading process as a risk hedging tool. To verify the effectiveness of the proposed strategy, usages of local renewable energy, economic benefits and success rates of transaction is compared against the traditional method for various cases.

<p dir="ltr">This paper proposes a bi-level peer-to-peer (P2P) multi-energy trading framework for a coupled distribution network (DN) and district heating network (DHN). At the lower level, each nodal agent represents its intra-nodal prosumers to optimize the local energy scheduling and P2P trading strategies based on the modified Nash bargaining theory, and a distributed algorithm is then adopted to enable individual agents to make their strategies autonomously only with the sharing of trading information. Once the lower-level P2P bargaining is settled, each agent is required to submit its nodal net loads and trading adjustment tolerances to the network operators. At the upper level, the network operators minimize the line power losses while satisfying network operation constraints by reconfiguring the DN and DHN as well as enforcing necessary trading adjustments from the lower-level agents when the network violations incurred by the P2P trading cannot be fully solved by network reconfiguration. Mathematically, the DN operation is modelled based on the linearized DistFlow with a set of new radiality constraints to sufficiently ensure the radial structure of the DN. The DHN operation is formulated as a quasi-linear thermal flow model independent of mass flow rate and water temperature, by which the computation complexity and limitations associated with traditional DHN formulations are addressed. Finally, a multi-energy network consisting of an IEEE 33-bus DN and a 23-node DHN is used to demonstrate the effectiveness of the proposed P2P trading framework and the efficiency of the solution algorithms.</p>

Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory