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Abstract Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study.

To sustain operation and maintenance of an active distribution network (ADN), a network fee should be charged by the distribution network service provider (DNSP) for facilitating the P2P energy trading service. To this end, this paper models the interaction among the DNSP and multiple prosumers as a Stackelberg game, and then develops a non-iterative and decentralized transactive mechanism to simultaneously achieve optimal network utilization pricing and peer-to-peer (P2P) trading. Simulation results in an ADN with four prosumers connected to a common substation bus validate the effectiveness and efficiency of the proposed scheme.

An optimal planning method for an integrated energy system (IES) considering electric vehicles (EVs) swapping station (SS) and carbon capture power system (CCPS) is studied in this paper. Firstly, based on the analysis of swapping EV load, the model of SS is established. Then, we built the models of CCPS, and other energy supply and storage equipment in IES. Based on this, the annual comprehensive cost of planning is regarded as the optimization objective, a planning model of IES considering SS and CCPS is developed, and solved by CPLEX. Finally, an actual case was analyzed to verify the feasibility and effectiveness of the planning method proposed in this paper.

The widespread use of distributed energy sources provides exciting potential for demand-side energy sharing and collective self-consumption schemes. Demand-side energy sharing and collective self-consumption systems are committed to coordinating the operation of distributed generation, energy storage, and load demand. Recently, with the development of Internet technology, sharing economy is rapidly penetrating various fields. The application of sharing economy in the energy sector enables more and more end-users to participate in energy transactions. However, the deployment of energy sharing technologies poses many challenges. This paper comprehensively reviews recent developments in demand-side energy sharing and collective self-consumption schemes. The definition and classification of sharing economy are presented, with a focus on the applications in the energy sector: virtual power plants, peer-to-peer energy trading, shared energy storage, and microgrid energy sharing cloud. Challenges and future research directions are thoroughly discussed.

Cooperation between electric power networks (EPNs) and district heating networks (DHNs) has been extensively studied under the assumption that all information exchanged is authentic. However, EPNs and DHNs belonging to different entities may result in marketing fraud. This paper proposes a cooperation mechanism for integrated electricity-heat systems (IEHSs) to overcome information asymmetry. First, a fraud detection method based on multiparametric programming with guaranteed feasibility reveals the authenticity of the information. Next, all honest entities are selected to form a coalition. Furthermore, to maintain operational independence and distribute benefits fairly, Benders decomposition is enhanced to calculate Shapley values in a distributed fashion. Finally, the cooperative surplus generated by the coalition is allocated according to the marginal contribution of each entity. Numerical results show that the proposed mechanism stimulates cooperation while achieving Pareto optimality under asymmetric information.

Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems. By considering the wind uncertainty and both binary and continuous decisions of quick-start generation units within the intraday dispatch, we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment (NCUC) problem with mixed-integer recourse. We propose two feasible frameworks for solving the optimization problem. One approximates the continuous support of random wind power with a finite number of events, and the other leverages the extremal distributions instead. Both solution frameworks rely on the classic nested column-and-constraint generation (C&CG) method. It is shown that due to the sparsity of $L_{1}$-norm Wasserstein metric, the continuous support of wind power generation could be represented by a discrete one with a small number of events, and the rendered extremal distributions are sparse as well. With this reduction, the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks. Numerical studies are carried out, demonstrating that the model considering quick-start generation units ensures unit commitment (UC) schedules to be more robust and cost-effective, and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks (ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming (SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems (ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm (PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

This paper studies the optimal operation of integrated system from the perspective of the demand side load shifting, aims at adapting to the unbalance between energy demand and supply in the combined cooling, heating and power (CCHP) microgrid. A shiftable electrical load model is proposed, which combined with the time-of-use (TOU) price strategy and environmental benefits of solar and wind power. Based on the TOU price strategy, the shiftable load models are established according to the user’s room temperature comfort demand and the allowable fluctuation range of the domestic hot water’s temperature, respectively. Then the optimization model is constructed, which takes into account factors such as economy and environment, and solved by a CPLEX solver. Finally, in order to deeply study the influence of TOU price on the system, the influences of heating/cooling and electrical load shift on the CCHP microgrids with different auxiliary heating sources are studied. The results show that the influence of Shiftable loads in the system using an electric boiler as an auxiliary heating source is significantly better than using a gas boiler; the proposed shiftable load models can reduce the operational cost by up to 17.02%; and have better effect than the existing one that only considers TOU.

Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples.

A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed. To obtain the preferred models, this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data, and carries out a comprehensive evaluation. First, according to Dempster-Shafer (D-S) evidence theory, a new accuracy indicator based on the relative error (RE) is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time. Next, a new dimensionless method is proposed, which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator, to avoid the conflict between pieces of evidence caused by the minimum value of zero. On this basis, the evidence fusion is used to obtain the comprehensive evaluation value of each model. Then, the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described. Finally, the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Solving the energy crisis and environmental pollution requires large-scale access to distributed energy and the popularization of electric vehicles. However, distributed energy sources and loads are characterized by randomness, intermittence and difficulty in accurate prediction, which bring great challenges to the security, stability and economic operation of power system. Therefore, this paper explores an integrated energy system model that contains a large amount of new energy and combined cooling heating and power (CCHP) from the perspective of automatic generation control (AGC). Then, a gradient Q(σ,λ) [GQ (σ,λ)] algorithm for distributed multi-region interconnected power system is proposed to solve it. The proposed algorithm integrates unified mixed sampling parameter and linear function approximation on the basis of the Q(λ) algorithm with characteristics of interactive collaboration and self-learning. The GQ (σ,λ) algorithm avoids the disadvantages of large action spaces required by traditional reinforcement learning, so as to obtain multi-region optimal cooperative control. Under such control, the energy autonomy of each region can be achieved, and the strong stochastic disturbance caused by the large-scale access of distributed energy to grid can be resolved. In this paper, the improved IEEE two-area load frequency control (LFC) model and the integrated energy system model incorporating a large amount of new energy and CCHP are used for simulation analysis. Results show that compared with other algorithms, the proposed algorithm has optimal cooperative control performance, fast convergence speed and good robustness, which can solve the strong stochastic disturbance caused by the large-scale grid connection of distributed energy.

Clean energies such as wind energy and solar energy have increased very fast in order to meet the environmental requirements. However, due to the uncertainties of wind and solar energy, the large-scale integration of new energy presents a great challenge to the power system economic dispatch. Thus, a multi-time-scale robust economic dispatch strategy of a multi-source hybrid power system based on the variable confidence level is proposed. The deterministic constraints of each time scale are transformed into robust constraints that take the uncertainty into account. Meanwhile, the robust level whose confidence level increases with the shortening of time scale is set to improve the scheduling conservative degree step by step. The selection principle of the robust level of each time scale is also presented. The proposed approach is applied to an IEEE 9-bus system. The calculation results are compared with those from the traditional multi-time scheduling method and show the effectiveness of the paper, which can reduce the uncertainty impact of wind, solar, and load forecast, as well as achieve a great balance of security, economic, and environmental benefits.