The variability caused by the proliferation of distributed energy resources (DERs) and the significant growth in unbalanced three-phase loads pose unprecedented challenges to distribution network operations. This paper focuses on how a distribution system operator (DSO), taking over the distribution grid and market operations, would develop a risk-aware flexibility market to mitigate uncertainties in an unbalanced three-phase power distribution network. First, a distributionally robust chance constraint (DRCC) method is devised to solve the unbalanced three-phase optimal power flow using a semidefinite programming (SDP) model. The DSO can apply the proposed solution to jointly clear energy and flexibility markets. Then, the DRCC model accuracy is improved by an information-sharing mechanism characterized by spatially-correlated uncertainties in the distribution grid. Further, a novel system-wide response function is derived to make the DRCC model tractable. Using the duality theory, the paper further investigates the physical composition of the DSO's cleared flexibility prices to guide the unbalanced distribution network operation. Finally, the effectiveness of the risk-aware flexibility market is verified in a modified three-phase IEEE 34-node system. Results demonstrate that the flexibility market can quantify the impact of spatially correlated uncertainties and facilitate the utilization of flexible resources to mitigate uncertainties across the network.

The growing uncertainty and the variability arising from the continuous proliferation of variable renewable deployment in electric vehicle charging stations (EVCSs) and power distribution network (PDN) have posed inevitable cost and operation risks to EVCSs and PDN, respectively. In such an intricate uncertain environment, how to coordinate the peer-to-peer (P2P) transactive energy (TE) trading among EVCSs is still a challenge. Therefore, this paper proposes a risk-aware P2P-TE coordination framework for EVCSs in constrained urban transportation network (UTN) and PDN to handle various uncertainties. The electric vehicle (EV) charging load simulation is implemented in UTN using Monte Carlo method (MCM) to obtain the expected value and various scenarios, which are used for EVCS scheduling and uncertainty set construction. The autonomous and privacy-protected P2P-TE trading which is modeled as a distributionally robust optimization (DRO) problem is designed among EVCSs considering the uncertainties of charging load, renewable generation and PDN electricity price. After receiving the trading results, a three-phase unbalanced probabilistic optimal power flow based on distributionally robust chance constraint (DR-CC) is executed by distribution system operator (DSO) for security operation, where the affine policy and system-wide response functions are developed to reformulate the original DR-CC model into tractable second-order cone programming (SOCP) form. If any PDN constraints are violated, a trading adjustment signal will be returned to EVCSs in an iterative process. Numerical tests are carried out on a 12-node UTN and the modified IEEE 33-bus PDN to validate the effectiveness of proposed risk-aware P2P-TE coordination scheme, where the total operation cost of EVCSs is reduced by 26.65% and the trade-off between conservatism and optimism against uncertainties is guaranteed.

This article proposes a battery energy storage (BES) planning model for the rooftop photovoltaic (PV) system in an energy building cluster. One innovative contribution is that a energy sharing mechanism is integrated with the BES planning model to study cooperative benefits between the PV owner and users, and meanwhile facilitate the reasonable installation of BES. In particular, the conditional value-at-risk (CVaR) is introduced to characterize the individual risk-preference degree of users against the uncertainties of electricity price. To enhance the computation efficiency and capture the interaction between the PV owner and users, the original problem is decomposed into a major energy trading problem and an additional payment bargaining problem. Specifically, the energy trading problem is solved by a distributed way to find the optimal BES sizing and energy sharing profiles; while an effective analytical method is derived to determine the associated payment scheme. Case studies verify the feasibility and effectiveness of the proposed model. The simulation results indicate that the cooperation operation increases revenues for the PV owner, decreases costs for users, and reduces the peak-to-valley difference of system load. The optimal sizing of BES is mainly affected by the scale of PV generation and the energy trading mode. In addition, it is proved that the proposed algorithm can effectively obtain the global optimal solution.

The electrical vehicle charging station (EVCS) paradigm will get to be more proactive progressively owing to the massive deployment of onsite distributed renewable energy (DRE) and battery energy storage system (BESS). In this environment, the coordination of peer-to-peer (P2P) transactive energy (TE) trading among EVCSs would be a critical topic which would demand further investigation. This paper proposes a hierarchical framework for the optimal P2P trading among EVCSs in constrained urban transportation network (UTN) and power distribution network (PDN). The UTN operation is incorporated through multi-period traffic flow assignment problem (MTAP), which is solved by the transportation system operator (TSO). After receiving the corresponding EV assignments, a P2P trading approach is considered among EVCSs, which would emphasize the EVCS privacy while minimizing the TE trading cost. The trading results are subsequently delivered by EVCSs to the distribution system operator (DSO) which would solve a three-phase unbalanced optimal power flow to guarantee the secure PDN operation. If any PDN constraints are violated, a trading adjustment signal will be returned to each EVCS in an iterative process. Numerical experiments based on EVCSs located on a 12-node UTN and the modified IEEE 33-bus PDN validate the effectiveness of the hierarchical P2P coordination approach and solution methodology, where the total operation cost of EVCSS is reduced by 16.61%.

As distributed renewable energy (DRE) generation technologies advance and transportation electrification deepens, electric vehicle charging stations (EVCSs) equipped with DRE will quickly proliferate in the near future. This paper, thus, proposes a distributed energy trading framework for EVCSs equipped with distributed photovoltaic (PV) system considering transportation network (TN). The framework contains the electric vehicle allocation problem (EAP) in TN and the distributed energy trading (DET) problem among EVCSs. The target of EAP is minimizing the whole time cost of TN. Therefore, the EV quantities in each EVCS can be obtained after solving the EAP problem by introducing the time cost coefficient. As for DET, this paper develops a distributed trading method among EVCSs derived from the Nash bargaining (NB) theory. The method considers not only the benefit of each participant itself, but also the fair distribution of the mutual profits among the participants at the same time. Numerical simulations are performed to validate the effectiveness and viability of the mentioned framework and models. The numerical results show that the EAP model can reasonably allocate the electric vehicles (EVs) that need to be charged. The operating profits of each EVCS can be significantly promoted by the DET among EVCSs. In addition, the proposed distributed solution methodology can efficiently address DET problems and protect the business privacy of participants. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This paper proposes a charging navigation strategy of electric vehicles (EVs) considering time-of-use (TOU) pricing, which takes into consideration both the charging demand of EV users and the revenue of EV charging station (EVCS) operators. Firstly, a spatial-temporal distribution of EVs in a day is given by a traffic simulation method. Then, by considering the impact of TOU price on charging navigation, an EV strategy including charging probability, charging energy, and charging station selection is proposed to minimize the costs of EVs. Based on the EV strategy, the optimal TOU charging price is formulated by EVCS operators to maximize the revenue under the given pricing rule. The simulation results show that the proposed method is not only beneficial to EVs and EVCSs, but also can effectively reduce the peak-valley difference of load profile and achieve the goal of peak load shifting.

Currently, rooftop photovoltaic (PV) generation on the residential user side has been rapidly developed due to technological progress and related costs reductions. Innovative business models need to be explored to make full use of distributed PV generation. Here, we design a energy trading framework for a residential building cluster consisting of a PV owner and multiple buildings. The Nash bargaining theory is used to solve the cooperative operation problem between the PV owner and buildings. In order to protect individual privacy, a distributed solution algorithm is employed to solve the power sharing problem. And an analytical method is proposed to solve the additional payment bargaining problem. The results show that all participants can benefit from energy sharing and the rationality of the proposed transaction framework is proven. In addition, our results show peak to valley difference of the electricity load can be reduced to 24.3%.

In recent years, carbon neutrality and sustainable development have become the key issue of social construction and development. The traditional single energy system can not meet the needs of energy sustainable development under the background of energy shortage because of its low energy efficiency. Multi-energy system generally includes electricity energy subsystem, thermal energy subsystem (heating and cooling), gas energy subsystem and other energy subsystems. The integrated energy system (IES) proposed in this paper integrates the energy of electricity, gas, heating, and cooling. At the same time, this paper proposes an integrated energy scheduling mechanism based on Vickrey-Clarke-Groves (VCG) auction. In the auction, users submit their energy demand data to the auctioneer, and then the energy hub (EH) provider completes the energy allocation through optimal scheduling, and decides the amount of payment of users, which proves that the auction mechanism has incentive compatibility property. It means that users will report their energy demand data. Cases studies are conducted to evaluate the energy optimization performance of the IES, and verify the economy of the auction mechanism.

随着分布式发电技术的不断成熟及发展,未来综合能源服务将是整合不同类型分布式发电并满足用户不同用能需求的有效途径.提出了一种含有多种分布式发电资源同时考虑多用能需求的综合能源服务商优化运行策略模型.首先建立了含有风电、光伏、燃气轮机、电储能、电热泵、辅助锅炉等分布式资源及电、热用能需求的园区综合能源系统优化调度模型;其次计算优化运行后的能源利用效率;最后,分析对比了不同季节、实时电价及天然气价格变化对综合能源服务商运行策略及盈利的影响.仿真结果验证了模型的有效性,其中综合能源服务商的收益对天然气的价格变化更为敏感.

当风力发电商(WPG)和电动汽车(EV)聚合商组成的虚拟电场(VPP)参与市场投标时,风电出力的不确定性、预测出力偏差以及市场价格的波动性,都是VPP在参与市场投标时需要考虑的因素.在计及上述因素的影响下,文中研究了由WPG和EV聚合商组成的VPP在日前市场和实时市场的联合竞价模型:假定VPP是价格的接受者,综合考虑日前和实时价格的不确定性,在日前市场中根据风电出力和市场价格的预测结果进行日前竞价,然后在实时市场上参与实时竞价.VPP不但可以通过EV充放电平抑WPG投标偏差,还可以根据价格信号进行充放电投标,实现削峰填谷.通过引入偏差考核机制,在日前和实时市场结束后进行统一结算.基于合作博弈理论,利用Shapley值法将总收益在WPG和EV之间根据各自的贡献进行合理分配.最后,通过算例验证了模型的可行性和有效性,结果表明VPP参与日前和实时市场可以增加收益,降低出力和价格不确定性带来的风险,为新能源参与现货市场的建设提供参考.

随着可再生能源发电技术的成熟及安装运行成本的不断降低,分布式发电得到了快速的发展,并由此产生了大量的电力产消者.然而分布式发电由于容量小、波动性大、分布零散等特点,并不适合参与现行的电力市场.针对此问题,文中结合国外电力零售市场研究和中国分布式电力交易的实际情况提出了一种配电网层面下的分散式电力市场模式,并分别从市场定义、参与主体、交易方式、时间尺度、出清方式等方面进行了分散式电力市场的框架设计.然后,针对分散式电力市场交易平台难以建立的问题,研究了将区块链技术应用于分散式电力市场构建的可行性及交易实现流程.最后,分别从政策法规和市场建设要点2个方面分析了构建分散式电力市场需要注意的问题.

With the implementation of China's renewable portfolio standard(RPS), the positive externalities of renewable energy will be fully reflected. And inter-provincial renewable energy transactions will become an important way for provinces without sufficient renewable energy output to complete the assessment of consumption responsibility. The key issue is that inter-provincial renewable energy dealers can act as agents for market entities in the province to purchase renewable energy from other provinces. Therefore, based on the two-level electricity market and using the conditional value-at-risk(CVaR) method, this paper establishes a nonlinear bilevel optimization model that considers the risk of electricity purchase, and then introduces the risk aversion coefficient to transform the upper-level multi-objective problem into a single-objective problem, using Karush-Kuhn-Tucker(KKT) conditions and Lagrange duality theory to convert the above bilevel nonlinear problem into a single-layer linear problem for solution. The case studies show the effectiveness of the model and the reduction of market operating costs.

Balancing energy generation and consumption is essential for smoothing the power grids. The mismatch between energy supply and demand would not only increase the cost on both sides, but also has a great impact on the stability of the system. This paper proposes a novel energy sharing mechanism (ESM) to facilitate the consumption of local energy. With the help of the ESM, multiple prosumers have an opportunity to share surplus energy with neighboring prosumers. The problem is formulated as a leader–follower framework based on the Stackelberg game theory. To address the aforementioned problems, a deep deterministic policy gradient (DDPG) is applied to solve the Nash equilibrium (NE). The numerical results demonstrate that the proposed method is more stable than the conventional reinforcement learning (RL) algorithm. Moreover, the proposed method can converge to NE and find a relatively good energy sharing (ES) pricing strategy without knowing the specific system information. In short, it is notable that the proposed ESM can be seen as a win–win strategy for both prosumers and the power system.

在中国电力消纳保障机制和新配额制的实施背景下,为了使省内消纳责任主体完成消纳考核,如何利用市场机制激励可再生能源跨省跨区消纳是关键问题之一.为此,借助于省间-省内两级市场,引入了省间可再生能源交易商代理省内消纳责任主体参与省间可再生能源市场交易.在两级市场框架下,以市场运行成本最小化为目标,建立了一种非线性的双层优化消纳模型.上、下层的出清结果作为对方层级的计算参数,利用KKT(Karush-Kuhn-Tucker)条件和拉格朗日对偶理论,将上述双层非线性问题转化为单层线性问题求解.最后,为验证所提模型的有效性对某地区进行算例仿真,分析不同消纳责任权重、绿证价格下的市场运行成本,结果表明两级市场能够有效降低省内消纳责任主体完成考核的市场成本,促进可再生能源的跨省跨区消纳和电力市场的绿色、经济运行.