Search Results (437)

With the widespread adoption of electric vehicles, commercial charging stations (CCS) have grown rapidly as a core component of charging infrastructure. Due to the concentrated and high-power charging load characteristics of CCS, a ‘peak on peak’ phenomenon can occur in the power distribution network. Demand response (DR) serves as an important and flexible regulation tool for power systems, offering a new approach to addressing this issue. However, when CCS participates in DR, it faces a dual dilemma between operational revenue and user satisfaction. To address this, this paper proposes a bi-level, multi-objective framework that co-optimizes station profit and nonlinear user satisfaction. An asymmetric sigmoid mapping is used to capture threshold effects and diminishing marginal utility. Uncertainty in users’ charging behaviors is evaluated using a Monte Carlo scenario simulation together with chance constraints enforced at a 0.95 confidence level. The model is solved using the fast non-dominated sorting genetic algorithm, NSGA-II, and the compromise optimal solution is identified via the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Case studies show robust peak shaving with a 6.6 percent reduction in the daily maximum load, high satisfaction with a mean of around 0.96, and higher revenue with an improvement of about 12.4 percent over the baseline. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

To enhance distribution network flexibility and economy under conditions involving a high penetration of distributed energy resources, this paper proposes a two-stage optimization method considering demand response (DR). The first stage establishes a marginal cost-based DR model using a “base compensation + increasing marginal cost” mechanism to curb irrational user behaviors, reducing peak-hour power purchase costs. The second stage develops a dynamic reconfiguration model minimizing network losses, voltage deviation, and switch operation costs. Solved by an Improved Grey Wolf Optimizer (IGWO), it incorporates a segmented voltage compensation mechanism quantifying user satisfaction through differentiated coefficients. The two stages operate in a coordinated framework where “temporal load optimization” informs “spatial topology reconfiguration”. Case results demonstrate that this coordinated approach significantly reduces power purchase costs, improves voltage quality, and minimizes network losses, providing an effective solution for efficient distribution network operation. Full article

Accurately identifying electric vehicle (EV) users with high demand response (DR) potential is critical for grid stability but remains challenging due to behavioral heterogeneity, data sparsity, and the subjectivity of expert-dependent methods. In particular, the absence of behavior labels and the low temporal frequency of EV charging events limit the effectiveness of conventional rule-based and clustering approaches. To address these issues, we propose a novel unsupervised framework that integrates deep behavioral modeling with multi-source indicator fusion. Our approach begins by developing a behavior recognition model robust to sparse data, effectively characterizing user charging patterns. Subsequently, a multi-dimensional potential feature system is established. A key innovation lies in our unsupervised weighting mechanism, which automatically learns the importance of each indicator by assessing inter-indicator correlations, thereby eliminating subjective bias. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank users comprehensively based on their fused potential scores. Case studies on a large-scale real-world EV charging dataset demonstrate that the proposed method can effectively distinguish high-potential users from low-potential ones. The results indicate clear separability across multiple behavioral and willingness-related dimensions. This provides a practical and data-driven basis for targeted DR incentive design and user-side flexible resource planning. Full article

This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative energy service applications to achieve proactive Demand Response (DR) and optimized usage of Renewable Energy Sources (RES). The proposed DT model is designed to digitally represent occupant behaviors and energy consumption patterns using Artificial Neural Networks (ANN), which enable continuous learning by processing real-time and historical data in different pilot sites and seasons. The DT development incorporates the International Energy Agency (IEA)—Energy in Buildings and Communities (EBC) Annex 66 and Drivers-Needs-Actions-Systems (DNAS) framework to standardize occupant behavior modeling. The research methodology consists of the following steps: (i) a mock-up simulation environment for three pilot sites was created, (ii) the DT was trained and calibrated using the artificial data from the previous step, and (iii) the DT model was validated with real data from the Alginet pilot site in Spain. Results showed a strong correlation between DT predictions and mock-up data, with a maximum deviation of ±2%. Finally, a set of selected Key Performance Indicators (KPIs) was defined and categorized in order to evaluate the system’s technical effectiveness. Full article

Accurate evaluation of demand response (DR) potential at the individual user level is critical for the effective implementation and optimization of demand response programs. However, existing data-driven methods often suffer from insufficient feature representation, limited characterization of load profile dynamics, and ineffective fusion of heterogeneous features, leading to suboptimal evaluation performance. To address these challenges, this paper proposes a novel demand response potential evaluation method based on multivariate heterogeneous features and a Stacking-based ensemble mechanism. First, multidimensional indicator features are extracted from historical electricity consumption data and external factors (e.g., weather, time-of-use pricing), capturing load shape, variability, and correlation characteristics. Second, to enrich the information space and preserve temporal dynamics, typical daily load profiles are transformed into two-dimensional image features using the Gramian Angular Difference Field (GADF), the Markov Transition Field (MTF), and an Improved Recurrence Plot (IRP), which are then fused into a single RGB image. Third, a differentiated modeling strategy is adopted: scalar indicator features are processed by classical machine learning models (Support Vector Machine, Random Forest, XGBoost), while image features are fed into a deep convolutional neural network (SE-ResNet-20). Finally, a Stacking ensemble learning framework is employed to intelligently integrate the outputs of base learners, with a Decision Tree as the meta-learner, thereby enhancing overall evaluation accuracy and robustness. Experimental results on a real-world dataset demonstrate that the proposed method achieves superior performance compared to individual models and conventional fusion approaches, effectively leveraging both structured indicators and unstructured image representations for high-precision demand response potential evaluation. Full article

To address the limitations of existing power system low-carbon dispatching studies—such as over-reliance on generation-side carbon mitigation, price-oriented demand response (DR) failing to guide carbon reduction, and the low solution efficiency of traditional carbon emission flow (CEF)-based two-stage models—this paper proposes a data-driven CEF framework integrated with a bi-level economic and low-carbon dispatching model. First, a data-driven CEF calculation method is developed: It eliminates the need for complex power flow post-processing while maintaining calculation accuracy through multiple linear regression. On this basis, a bi-level optimization model is constructed: The upper level focuses on optimizing the economic and low-carbon objectives of power grid operation, while the lower level regulates industrial, commercial, and residential load aggregators (LAs) via carbon-intensity-oriented DR strategies and economic compensation mechanisms. Finally, a sample-based optimization algorithm combined with convex relaxation is proposed to solve the model, avoid the static setting of power flow and carbon intensity, and improve solution efficiency. Case studies demonstrate the following: the proposed method reduces the calculation time of node carbon intensity from 5 min to less than 100 ms, with the coefficient of determination (R²) ranging from 0.969 to 0.998; compared with the two-stage method, it achieves a 4.26% reduction in total scheduling cost, a 3.80% decrease in total carbon emissions, a 53.27% drop in carbon trading cost, and a 21.6% shortening in iteration time. These results verify that the proposed method can effectively enhance the source−load interaction and improve the accuracy and efficiency of low-carbon scheduling. This study provides a feasible technical path for the low-carbon transition of new-type power systems. Full article

The uncertainty of baseline load forecasting critically influences both the assessment of load reduction potential and demand response (DR) settlement. Therefore, this paper focuses on assessing load reduction potential based on probabilistic predictions of the baseline load. First, the uncertainty of the baseline load prediction is analyzed through calculating the conditional probability density function (PDF) and interval estimation of baseline load prediction errors from the convolutional neural network (CNN) model. Then, the probabilistic model of load reduction potential is proposed based on the results from the probabilistic prediction of baseline load and the terms about the interruptible load in DR contracts. Finally, the Monte Carlo simulation method is used to assess the load reduction potential, and probability distributions of the load reduction states, the lower and upper limits of the load reduction potential, are analyzed. Case studies demonstrate that the proposed method effectively characterizes the uncertainty of prediction results, with the prediction interval normalized average width (PINAW) decreased by 10.97%, thereby enabling the effective assessment of load reduction potential from the probabilistic perspective, helping decision makers take better choices. Full article

This paper introduces a novel stochastic multi-objective optimization framework for the integration of gravity energy storage (GES) with renewable resources—photovoltaic (PV) and wind turbine (WT)—in distribution networks incorporating demand response (DR), addressing key gaps in uncertainty handling and optimization efficiency. The GES plays a pivotal role in this framework by contributing to a techno-economic improvement in distribution networks through enhanced flexibility and a more effective utilization of intermittent renewable energy generation and economically viable storage capacity. The proposed multi-objective model aims to minimize energy losses, pollution costs, and investment and operational expenses. A new multi-objective enhanced weighted average algorithm integrated with an elite selection mechanism (MO-EWAA) is proposed to determine the optimal sizing and placement of PV, WT, and GES units. To address uncertainties in renewable generation and load demand, the two-point estimation method (2m + 1 PEM) is employed. Simulation results on a standard 33-bus test system demonstrate that the coordinated use of GES with renewables reduces energy losses and emission costs by 14.55% and 0.21%, respectively, compared to scenarios without storage, and incorporating the DR decreases the different costs. Moreover, incorporating the stochastic model increases the costs of energy losses, pollution, and investment and operation by 6.50%, 2.056%, and 3.94%, respectively, due to uncertainty. The MO-EWAA outperforms conventional MO-WAA and multi-objective particle swarm optimization (MO-PSO) in computational efficiency and solution quality, confirming its effectiveness for stochastic multi-objective optimization in distribution networks. Full article

This paper presents a comprehensive framework for real-time energy management in microgrids integrating distributed renewable energy sources and demand response (DR) programs. To address the inherent uncertainties in key operational variables—such as load demand, wind speed, solar irradiance, and electricity market prices—this study employs a probabilistic modeling approach. A two-stage stochastic optimization method, combining mixed-integer linear programming and optimal power flow (OPF), is developed to minimize operational costs while ensuring efficient system operation. Real-time dynamic pricing mechanisms are incorporated to incentivize consumer load shifting and promote energy-efficient consumption patterns. Three microgrid scenarios are analyzed using one year of real historical data: (i) a grid-connected microgrid without DR, (ii) a grid-connected microgrid with 10% and 20% DR-based load shifting, and (iii) an islanded microgrid operating under incentive-based DR contracts. Results demonstrate that incorporating DR strategies significantly reduces both operating costs and reliance on grid imports, especially during peak demand periods. The islanded scenario, while autonomous, incurs higher costs and highlights the challenges of self-sufficiency under uncertainty. Overall, the proposed model illustrates how the integration of real-time pricing with stochastic optimization enhances the flexibility, resilience, and cost-effectiveness of smart microgrid operations, offering actionable insights for the development of future grid-interactive energy systems. Full article

Given the uncertainty from renewable production, local loads and battery operating states in microgrid, it is vital to develop an efficient energy management scheme to improve system economics and enhance grid reliability. In this paper, we consider a renewable integrated microgrid scenario including an energy storage system (ESS), bidirectional energy flow from/to conventional power grid and ESS health estimation. We develop a novel demand response-based scheme for microgrid energy management with a long short-term memory (LSTM) network and reinforcement learning (RL), aiming to improve the system operating profit from energy-trading and reduce the battery health cost from energy-scheduling. Specifically, to overcome the uncertainty from future, we utilize LSTM to forecast the unknown demand and electricity price. To obtain the desired ESS control scheme, we apply RL to learn an optimal energy-scheduling strategy. To improve the critical performance of the RL paradigm, we propose a random greedy strategy to encourage exploration. Numerical results show that our proposed algorithm outperforms the baselines by improve the system operating profit by 8.27% and 17.31% while ensuring ESS operating safety. By integrating energy efficiency with sustainable energy management practices, our scheme contributes to long-term environmental and economic resilience. Full article

In line with the global goal of achieving climate neutrality, a flexible energy system capable of accommodating the uncertainties induced by renewable energy sources becomes vitally important. This paper investigates the electricity demand flexibility characteristics and develops demand-response (DR) control strategies for grid-interactive buildings. First, a building’s flexible loads are classified into three types, interruptible loads (ILs), shiftable loads (SLs), and adjustable loads (ALs). The load flexibility characteristics, including real-time response capabilities, the time window range, and the adaptive adjustment ratios, are investigated. Second, DR control strategies and their features, which form the basis for achieving different optimization objectives, are detailed. Finally, three DR optimization objectives are proposed, including maximizing load reduction, maximizing economic benefits, and ensuring stable load reduction and recovery. Through case studies of a residential building and an office building, the results demonstrate the effectiveness of these DR strategies for load reduction and cost savings under different DR objectives. For the residential building, our results showed that over 50% of the electricity load could be shifted, resulting in electricity bill savings of over 17.6%. For office buildings, various DR control strategies involving zone temperature resetting, lighting dimming, and water storage utilization can achieve a total electricity load reduction of 28.1% to 63.6% and electricity bill savings of 7.39% to 26.79%. The findings from this study provide valuable benchmarks for assessing electricity flexibility and DR performance for other buildings. Full article

The shift toward decentralized energy systems and the rapid growth of renewable integration have brought renewed attention to the Multi-Area Economic Dispatch (MAED) problem. Unlike single-area dispatch, which focuses only on local balance, MAED must also coordinate inter-area exchanges, respect regional operating limits, and ensure overall reliability. This paper reviews both MAED and its dynamic extension, the Multi-Area Dynamic Economic Dispatch (MADED). The review examines core objectives—cost minimization, emission reduction, and renewable utilization—and surveys a wide range of solution methods. These include classical mathematical programming, metaheuristic and hybrid approaches, and more recent advances based on machine learning and reinforcement learning. Special emphasis is placed on uncertainty-oriented models that address demand variability, market dynamics, and renewable fluctuations. The discussion also highlights the role of Distributed Energy Resources (DERs), Energy Storage Systems (ESSs), and Demand Response (DR) in improving system flexibility and resilience. Despite notable progress, research gaps remain, including limited treatment of uncertainty, insufficient integration of DR, oversimplified modeling of electric vehicles, and the marginal role of reliability. To address these issues, a research agenda is proposed that aims to develop more adaptive, scalable, and sustainable dispatch models. The insights provided are intended to support both academic research and practical applications in the planning and operation of interconnected grids. Full article

In this paper, an emission- and reliability-aware stochastic optimization model is proposed for the economic planning of electric bus parking lots (EBPLs) with photovoltaic (PV) and wind-turbine (WT) resources in an 85-bus radial distribution network. The model simultaneously minimizes operating, emission, and energy-loss costs while increasing system reliability, measured by energy not supplied (ENS), and uses a fuzzy decision-making approach to determine the final solution. To address optimization challenges, a new multi-objective entropy-guided Sinh–Cosh Optimizer (MO-ESCHO) is proposed to efficiently mitigate premature convergence and produce a well-distributed Pareto front. Also, a hybrid forecasting architecture that combines MO-ESCHO and artificial neural networks (ANN) is proposed for accurate prediction of PV and WT power and network loading. The framework is tested across five cases, progressively incorporating EBPL, demand response (DR), forecast information, and stochastic simulation of uncertainties using a new hybrid Unscented Transformation–Cubature Quadrature Rule (UT-CQR) method. Comparative analyses against conventional methods confirm superior performance in achieving better objective values and ensuring computational efficiency. The outcomes indicate that the combination of EBPL with RES reduces operating costs by 5.23%, emission costs by 27.39%, and ENS by 11.48% compared with the base case with RES alone. Moreover, incorporating the stochastic model increases operating costs by 6.03%, emission costs by 5.05%, and ENS by 7.94% over the deterministic forecast case, reflecting the added complexity of uncertainty. The main contributions lie in coupling EBPLs and RES under uncertainty and proposing UT-CQR, which exhibits robust system performance with reduced variance and lower computational effort compared with Monte Carlo and cloud-model approaches. Full article

The transition to sustainable energy systems is essential for attaining global carbon neutrality targets. Demand-side flexibility for carbon mitigation is investigated, and a low-carbon operational strategy tailored for industrial park energy systems is proposed. Demand response (DR) is classified into price-based and alternative categories, with respective models developed utilizing a price elasticity matrix and accounting for electricity-to-heat conversion. Integrated energy system (IES) involvement in the carbon trading market is incorporated through a stepped carbon pricing mechanism to regulate emissions. A mixed-integer linear programming model is constructed to characterize IES operations under ladder-type carbon pricing and DR frameworks. The model is resolved via the off-the-shelf commercial solver, facilitating effective optimization of dispatch over multiple time intervals and complex market interactions. Case study findings indicate that implementing stepped carbon pricing alongside DR strategies yields a 44.45% reduction in carbon emission costs, a 9.85% decrease in actual carbon emissions, and a 10.62% reduction in total system costs. These results offer a viable approach toward sustainable development of IES, achieving coordinated improvements in economic efficiency and low-carbon performance. Full article