Climate change is progressively reshaping the spatiotemporal dynamics of renewable energy sources such as wind and solar, intensifying the complexity and uncertainty of long-term power system planning. Existing planning frameworks are largely focused on climate mitigation strategies but often overlook the critical dimension of climate adaptation, limiting their efficacy in managing evolving climatic risks. In response, this article proposes a multistage stochastic low-carbon planning framework that incorporates climate-related uncertainties into system planning decision-making. By embedding climate evolution trajectories into the planning horizon, the proposed approach determines optimal stage-wise planning pathways that jointly accommodate mitigation goals and adaptation imperatives under long-term climate uncertainties. First, a systematic climate uncertainty modeling approach is developed to capture both scenario uncertainty and climate response uncertainty through the construction of a representative scenario tree. Second, to reconcile the temporal mismatch between coarse-resolution climate projections and the finegrained requirements of power system planning, a climate-consistent temporal downscaling method is proposed to transform long-term climate projections into high-resolution, hourly level data. Third, to address the computational complexity inherent in the multistage planning problem, a tailored decomposition-based stochastic dual dynamic programming algorithm is developed, which operates on a stage-wise clustered scenario tree to leverage the tree’s structural compactness for accelerated convergence and scalable optimization under climate-related uncertainties. Numerical studies demonstrate that the proposed climate-adaptive planning framework enhances the power system’s ability to manage climate-induced risks while maintaining cost-effectiveness across a wide range of plausible climate futures.

The rapid development of electric vehicles raises a higher requirement for charging station operation and management. Therefore, this work proposes a data-driven method aimed at enhancing the economical operation of charging stations. Considering the privacy of charging data and the influence of traffic flow, this data-driven method simulates the spatiotemporal charging demand based on the predicted traffic flow. To obtain precise prediction, this work develops an efficient gated-attention spatiotemporal convolutional network (GSTCN) to explore the long-term spatial and temporal dependence of traffic flow. GSTCN is constructed by two main components: a spatial gated attention (SGA) unit and a temporal gated (T-Gated) attention layer. The spatial pattern of traffic flow is unearthed by the well-designed SGA unit, while the temporal correlation between different time steps is captured through the proposed T-Gated attention layer. Then, an energy storage system (ESS) is employed to improve the effective management of charging stations. Numerical results demonstrate the efficiency of GSTCN in charging station management. GSTCN can produce more accurate prediction data, which leads to a more economical operation of the energy storage system compared with the benchmarks.

Achieving carbon neutrality is widely recognized as the key measure to mitigate climate change. As the basis for achieving carbon neutrality, corporate carbon footprint (CCF) estimation is mainly based on the disclosed information of corporates to roughly estimate the direct carbon emission, but the estimation may not be comprehensive, timely, and accurate. In this article, the CCF estimation problem is formulated and a novel estimation methodology is proposed for the first time to estimate the direct and indirect carbon emissions of factories in real time. An appliance identification method based on the multihead self-attention mechanism and gated recurrent unit is proposed to identify the device states, and then, calculate the corresponding direct carbon emission. The indirect carbon emission is derived from the electricity consumption of the factory and the marginal carbon emission factor of the connected bus. A dataset containing load and device state data from six different industries is released and used to verify the effectiveness of the proposed method. Experiments show that the proposed appliance identification method is significantly superior to the benchmarks in the literature, and the proposed method can achieve a comprehensive and accurate estimation of the minute-level CCF.

To tackle the challenges introduced by the fast-growing charging demand of electric vehicles (EVs), the power distribution systems (PDSs) and fast charging stations (FCSs) of EVs should be planned and operated in a more coordinated fashion. However, existing planning approaches generally aim to minimize investment costs in PDSs while ignoring the risk of worsening traffic conditions. To overcome this research gap, this article integrates the interests of traffic networks into PDS and FCS joint planning model to mitigate negative impacts on traffic conditions caused by installing FCSs. First, a novel microscopic method that is different from traditional assignment methods is proposed to simulate the influences of FCSs on traffic flows and EV charging loads. Then, a multiobjective joint planning model is developed to minimize both the planning costs and unbalanced traffic flows. A new bilayer Benders decomposition algorithm is designed to solve the proposed joint planning model. Numerical results on two practical systems in China validate the feasibility of our microscopic method by comparing the simulated results with real data. Compared with existing approaches, it is also demonstrated that the proposed joint planning approach helps to balance traffic flow assignments and relieve traffic congestion.

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this article, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double-auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

Residential energy management (REM) is an important way to encourage users to reduce or shift energy demand with dynamic pricing. It could significantly affect the supply-demand relationship between electricity service providers (ESPs) and users, reduce energy cost and consumption, and contribute to sustainable development. To improve latency and processing performance, a three-tier edge-cloud collaborative REM (ECCREM) architecture is presented. In consideration of matching the architecture, a two-stage energy management mechanism is proposed with system reliability and resource utilization requirements taken into account. At the first stage, the interaction between real-time pricing and energy demand is modeled by a Stackelberg and Lyapunov-based pricing and energy demand joint optimization (SLPEDO) algorithm. At the second stage, two procedures, i.e., energy scheduling between a cloud tier and an access tier, and energy scheduling between an access tier and an infrastructure tier, are implemented. A priority-based demand ratio sequentially scheduling strategy is proposed to address energy scheduling in these two procedures, respectively. Simulation results show that compared with the existing demand ratio-based scheduling and equally scheduling strategies, the proposed strategy can improve overall satisfaction of users by up to 20%. In addition, energy cost can be reduced and demand fluctuation relieved.

A key feature of an electricity distribution market is that it may be dominated by renewable generation with zero marginal cost. Existing market mechanisms are likely to fail in this context since it cannot generate a reasonable price signal to compensate for the investment cost of renewable generators. Given this background, a double-sided auction market mechanism is presented for pricing the zero marginal cost renewable generation in the distribution system. Honesty is proved to be a dominant strategy for participants, which would enable the proposed mechanism to develop into a set-and-forget bidding market. The proposed market mechanism is also shown to be compatible with the nodal pricing system. Finally, case studies are carried out, and the results show that under the proposed market mechanism, the problem of always bidding a zero price by renewable generators in some existing markets can be avoided. Even when only renewable generation units with zero marginal costs participate in the bidding, the proposed mechanism can still produce a reasonable market clearing price. When adopting the average pricing market mechanism, merits of nodal pricing can still be retained and contribute to the enhancement of the operating efficiency of the distribution network.

The emerging concentrating solar power plant (CSPP) represents one of the promising technologies for promoting solar power applications. In this paper, risk-constrained day-ahead scheduling strategies for a virtual power plant (VPP) integrating a CSPP with some responsive residential and industrial loads are proposed considering the uncertainties from electricity price, thermal production of the solar field of the CSPP, and participation factor of residential demand response. The well-established information gap decision theory (IGDT) is utilized to hedge against the risk caused by these uncertainties. Based on IGDT, both a robust scheduling strategy for the risk-aversion decision maker and an opportunistic scheduling strategy for the opportunity-seeking decision maker are presented for hedging the profit risk of the VPP against variations of electricity price, thermal production, and demand response. Simulation results show that the presented IGDT-based method can act as an effective tool for managing risks from uncertainties, and also demonstrate that the RA VPP should focus more on the thermal production of the CSPP so as to guarantee the desired profit, whereas the OS VPP should pay more attention to the market price so as to achieve a windfall profit.

The emerging virtual power plant (VPP) could contribute to the security and economics enhancement of the power system concerned, and has attracted much attention in power system community. To explore the advantages of a VPP, optimal planning for the VPP including the configuration and placement of various components is the first and yet the most important step. The advantages of the VPP could be further promoted by introducing an appropriate demand response (DR) mechanism. Given this background, an optimal planning model for a VPP with demand response (DR) implemented is presented. First, the DR mechanism is implemented by optimizing charging loads of electric vehicles (EVs) through electricity price adjustments. An optimal planning model is then presented with the objective of minimizing the investment and operation costs, while considering the construction of wind power units and feeders, and solved by BONMIN in YALMIP/MATLAB environment. Finally, a sample system is employed to demonstrate the proposed model.

The wide-area measurement system provides a new data acquisition and supervisory control tool for a power system, and the data acquisition level is increased dramatically with its development in the smart grid environment. Huge data associated with the power system operation are acquired, which are beneficial for enhancing situational awareness of a power system concerned. Identifying the coherency among synchronous generators using real-time signals from phasor measurement units (PMUs) is one of the major tasks of situational awareness in power system operation. Given this background, a data-driven coherency identification methodology is proposed based on the spectral clustering algorithm. First, several trajectory dissimilarity indices for the rotor angle and rotor speed trajectories of generators as measured by PMUs are presented based on the trajectory similarity theory. Second, a decision-making method based on the Gini coefficient and Kendall rank correlation coefficient is presented for integrating multiple indices describing trajectory dissimilarities. Third, the spectral clustering algorithm is presented to identify the coherency of synchronous generators, and silhouette is presented for determining a reasonable number of coherent groups. Finally, oscillation events happened/simulated in two actual power systems, i.e., Guangdong power system in China and Western Interconnection power system in North America, are utilized to demonstrate the effectiveness of the proposed data-driven coherency identification methodology.

The network reconfiguration during power system restoration after blackouts usually takes a long and complex procedure. To address the uncertainties in the restoration steps and time involved, a CCP (Chance Constrained Programming) based method for network reconfiguration scheme optimization is proposed in this paper. The proposed method can generate the best restoration sequence to maximize the benefit of the reconfiguration scheme by taking into account the number of restarted generator-nodes and the cost of power outage saved by the load restoration accordingly. The Differential Evolution (DE) is employed to solve for the optimal solution subject to special requirements of the network reconfiguration operation. A numerical example over the New England 39-bus power system is conducted to demonstrate the effectiveness of the proposed method.

As plug-in hybrid electric vehicles (PHEVs) are expected to be widely used in the near future, a mathematical model is developed based on the traditional security constrained unit commitment (SCUC) formulation to address the power system dispatching problem with PHEVs taken into account. With the premise of power system secure operation, both the economic benefit for PHEV users and the carbon-emission costs are taken into account. Then, the features of PHEVs as mobile energy storage units are exploited to decouple the developed model into two sub-models, involving the unit commitment model and the charging and discharging scheduling model that includes AC power flow constraints. The optimal plug-in capacities for PHEVs and the schemes, including when and where charging and discharging occur, are obtained through a mixed integer programming algorithm and the Newton-Raphson load flow algorithm in addition to the optimal day-ahead unit commitment scheme. Finally, the feasibility and efficiency of the proposed model are verified with a 6-bus test system.

In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system.

A method is developed to explore the potential links between an oligopolistic electricity market and a competitive emission permit market in which permits allocated to those highly efficient generation companies (units) with lower emissions could be traded to other companies with higher emissions. The well-developed Cournot non-cooperative game model is employed to describe the behavior of power producers, and to determine the market equilibriums of generation outputs under complete and incomplete information. A numerical example with six power producers is employed to demonstrate the features of the developed model as well as to analyze the impacts of emission permit trading on the oligopoly electric market equilibriums.

Climate change has become a problem of extensive concern especially in developed countries. Emissions trading, in principle an efficient way to reduce the emissions of greenhouse gases, has been implemented in the European Union, and has been proposed in a number of other countries including Australia and the United States. The power industry is the largest emitter of greenhouse gases in many countries. The purpose of this paper is to briefly review the literature concerned with the potential impacts of emissions trading on power industries and electricity markets, including key issues of emissions trading scheme design, the methods of allowance allocation and their impacts on generation investments, renewable sources and electricity prices.

In the electricity market environment, it is very important for generation companies (GENCOs) to make the optimal mid-term generation operation planning (MTGOP) which includes the trading strategies in the spot market and the contract market as well as the suitable unit maintenance scheduling (UMS). In making the decision of MTGOP, GENCOs are subject to risk due to uncertain factors, and hence should manage the inevitable risk rationally. Given this background, a new MTGOP model is first developed for a GENCO as a price taker so as to maximize its profit and minimize its risk measured by the Conditional Value-at-Risk (CVaR). In this model, the bilateral physical contracts are taken into consideration, together with the transmission congestion and the operation constraints of generating units. Then, a solving method is given by integrating the Genetic Algorithm and the Monte Carlo method. Finally, a numerical example is used to show the features of the proposed method.

The employment of performance based regulation (PBR) in distribution systems could provide some incentive for improving operating efficiency and reducing electricity prices. However, if the PBR mechanism is not properly designed, the enforcement of the PBR may have a negative effect on the supply reliability. In this paper, a mathematical model for optimally setting the parameters of the PBR with a reward/penalty structure is presented, with the minimization of the costs associated with the enforcement of the PBR as the objective and the required reliability level for the distribution system operation as the constraint. Finally, the well-known genetic algorithm is employed for solving the optimization problem. The effectiveness of the approach is demonstrated on a sample example.