The increasing adoption of electric vehicles has led to new and unpredictable load conditions for electric vehicle charging stations (EVCSs), making charging demand forecasting important to the profitable deployment of EVCSs. Although existing spatial-temporal forecasting models have made significant progress, they ignore the realistic topologies of EVCS networks and the influence of external interference on charging demand. Moreover, limited research exists on developing forecasting models from the perspective of EVCSs participating in the energy market. This paper proposes a novel parallel-structured spatio-temporal mutual residual graph convolution-combined bi-long short-term memory for predicting the charging demand of EVCSs. First, a new mutual adjacency matrix considering both the static and dynamic attributes of EVCSs is constructed. This matrix is then combined with graph convolution and residual blocks to capture multi-level spatial dependencies and map relations between nodes and external factors. Second, to address temporal dependencies, CBi-LSTM combining Bi-LSTM with an additional predictor that considers day-type tendency features is developed. Finally, a parallel structure is adopted to obtain the final prediction results and preserve the integrity of spatiotemporal dependencies. Case studies validated the performance of the proposed model, which demonstrated high forecasting accuracy and the potential for profitable application in the energy market.

As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.

Owing to the increases of energy loads and penetration of renewable energy with variability, it is essential to determine the optimum capacity of the battery energy storage system (BESS) and demand response (DR) within the microgrid (MG). To accomplish the foregoing, this paper proposes an optimal MG operation approach with a hybrid method considering the game theory for a multi-agent system. The hybrid method operation includes both BESS and DR methods. The former is presented to reduce the sum of the MG operation and BESS costs using the game theory, resulting in the optimal capacity of BESS. Similarly, the DR method determines the optimal DR capacity based on the trade-off between the incentive value and capacity. To improve optimization operation, multi-agent guiding particle swarm optimization (MAG-PSO) is implemented by adjusting the best global position and position vector. The results demonstrate that the proposed approach not only affords the most economical decision among agents but also reduces the utilization cost by approximately 8.5%, compared with the base method. Furthermore, it has been revealed that the proposed MAG-PSO algorithm has superiority in terms of solution quality and computational time with respect to other algorithms. Therefore, the optimal hybrid method operation obtains a superior solution with the game theory strategy.

This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity.

The advancement of Internet of Things technologies has accelerated the development of virtual power plants; however, uncertainties within these systems can jeopardize their operational flexibility and profitability in electricity markets. To address these challenges, this study proposes a novel data-driven bidding strategy for virtual power plants in both day-ahead and real-time electricity markets, incorporating a hybrid forecasting model and an unsupervised learning-based customized incentive demand response. First, unlike single forecasting models that naively train original input features, a hybrid decomposition and deep learning-based forecasting model is proposed to efficiently handle irregular and volatile data sequences, thereby improving forecasting accuracy and reducing mismatch costs. Second, to address the inherent inflexibility of current incentive mechanisms for demand response, a new customized incentive demand response program is proposed to efficiently motivate demand response by offering customized incentive rates that simultaneously consider the cus-tomer diversity, market prices, and various stakeholder preferences. To bid in electricity markets, a multi-objective optimization is formulated in the day-ahead stage to account for interests of various stakeholders, while a deep learning-based rolling horizon optimization is executed in the real-time stage to minimize mismatch costs. Simulation results using real data from the Pennsylvania-New Jersey-Maryland market validate the superiority of the proposed data-driven bidding strategy for virtual power plants in both day-ahead and real-time electricity markets.

The advancement of Internet of Things technologies enables precise control over home appliances, leading to a grow-ing demand for demand response (DR) in residential areas. The electricity price mechanism has been an important means to implement residential DR, dynamically adjusting power demand to alleviate power mismatch in response to time-varying prices. However, despite various studies on pricing mechanisms, the unidirectional nature of these electricity pricing mechanisms greatly diminishes the willingness of end consumers to actively engage in housing DR as their electricity consumption behavior is not effectively reflected into their electricity pricing. To overcome these challenges, this study proposes a predictive home energy management system (PHEMS) by developing a new customized bi-directional real-time pricing (RTP) mechanism-based DR strategy by developing with an efficient price forecasting model. The pro-posed PHEMS first develops a new customized bi-directional RTP mechanism that enables the participation of end-users in formulating hourly RTPs based on their hourly shifted power and household flexible appliance for strong DR participation. Second, a new deep learning-based forecasting model namely, unshared convolutional neural network-nested long short-term memory, is employed to address both the spatial-temporal variabilities of the real-time price and support global real-time optimization. A rolling-horizon-based optimization strategy is then constructed to enable self-correction, ensuring robust and economic operation for residential end-users. The experimental results demonstrate the superiority of the proposed PHEMS in terms of the forecasting accuracy, peak reduction, valley filling, and electricity cost reduction, while ensuring user comfort.