Incentive-based demand response (DR) has been recognized as a powerful tool to mitigate supply-demand imbalance in electricity market. However, it cannot be directly applied into integrated demand response (IDR) for multi-energy systems where there are energy substitution effect among different types of energy carriers and behavioral coupling effect of consumers. In this paper, an incentive-based IDR model for multiple energy carriers considering behavioral coupling effect of consumers is proposed. The proposed model not only effectively deals with the above two effects but also enhances applicability of incentive-based DR into the scenario where curtailment DR and absorbing DR are planned simultaneously. Furtherly, the proposed IDR model is also extended to multi-period coupling IDR considering the effect of energy storage unit. The solution methodologies for the proposed model and extended model are also presented. The existence and uniqueness of optimal solution are identified. Finally, simulation results verify merits of the proposed model in cutting down total cost of multi-energy aggregator (MEA), reducing dissatisfaction of consumers, improving accuracy of optimal solution, and improving the utilization of energy storage unit.

Incentive-based integrated demand response (IDR) has been recognized as a powerful tool to mitigate supply-demand imbalance in integrated energy systems with high penetration of distributed renewable energy sources (RESs). However, incomplete information as well as demand-side substitute and complementary effect (S&C effect)—the change in demand for a type of energy carrier as a result of a change in the relative incentive rates of other kind of energy carriers—have been central challenges to estimate accurately the response behavior of consumers. This paper proposed an improved incentive-based IDR model to effectively cope with the S&C effect and incomplete information. Besides, both output uncertainty of RESs and responsiveness uncertainty of consumers are taken into account, with an improved model of energy storage unit to deal with balancing power deviation and measure corresponding risk costs caused by the uncertainties. The proposed IDR model is formulated as a bi-level stochastic programming problem, which is converted equivalently into a nonlinear convex optimization problem to solve it efficiently. Simulation results validate the effectiveness of the proposed method to cope with the S&C effect and incomplete information, verifying merits of the model in reducing both comfort loss of consumers and the total cost/risk cost of multi-energy aggregator (MEA), decreasing balancing power deviation, enhancing MEA’s ability of risk management, and promoting calculation efficiency.

It is a critical part of increasing renewable energy accommodation by using virtual power plant (VPP) to attain carbon neutrality. However, VPP applications primarily consider VPP's participation in power market transactions as a whole and rarely consider the transaction interaction between internal resources. VPP's internal resources complement each other organically, and blockchain technology for distributed transactions has incorporated points. In this article, the authors undertake a study and examine the P2P scenario of VPP internal transactions in light of the issues experienced by DERs transactions. Next, the authors analyse the consensus mechanism, smart contract, inter‐blockchain technology, and game theory, and how to apply them in the P2P scenarios of VPP internal transactions. Further, the authors design the function of the DER transaction system, which lays the foundation for the realisation of the system in the future. Finally, the authors conclude that the potential of blockchain technology in P2P transactions between internal entities of the VPP is significant and warrants further investigation.

Incentive‐based demand response (IBDR) has been recognized as a powerful tool to mitigate supply–demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncertainty and responsiveness uncertainty, have been a central challenge to implement IBDR programs. In this paper, a stochastic programming model for IBDR considering the complex uncertainties of consumers is proposed. The proposed model can effectively deals with the above two uncertainties. Besides, the model of energy storage unit (ESU) has been improved to cope with properly the deviation between total actual balancing power and required balancing power. Moreover, the model enhances the applicability of IBDR to be applicable to both curtailment IBDR programs and absorbing IBDR programs by adding dynamic parameters. The model is formulated as a bi‐level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results verify merits of the proposed model in cutting down total cost of DRA, decreasing risk cost of DRA and reducing balancing power deviation caused by uncertainty of consumers.

In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of accurately predicting users’ response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability.