Search Results (89)

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

The proposed study incorporates a stochastic demand side management (SDSM) strategy for a self-sufficient residential system powered from a PV source with a hybrid battery–hydrogen storage system to minimize the total degradation costs associated with key components, including Li-io batteries, fuel cells, and PEM electrolyzers. The uncertainty in demand forecasting is addressed through a scenario-based generation to enhance the robustness and accuracy of the proposed method. Then, stochastic optimization was employed to determine the optimal operating schedules for deferable appliances and optimal water heater (WH) settings. The optimization problem was solved using a genetic algorithm (GA), which efficiently explores the solution space to determine the optimal operating schedules and reduce degradation costs. The proposed SDSM technique is validated through MATLAB 2020 simulations, demonstrating its effectiveness in reducing component degradation costs, minimizing load shedding, and reducing excess energy generation while maintaining user comfort. The simulation results indicate that the proposed method achieved total degradation cost reductions of 16.66% and 42.6% for typical summer and winter days, respectively, in addition to a reduction of the levelized cost of energy (LCOE) by about 22.5% compared to the average performance of 10,000 random operation scenarios. Full article

In recent years, extreme natural disasters, such as typhoons, have become increasingly frequent, leading to persistent power outages in urban distribution grids. These outages pose significant challenges to the stability of urban power supply systems. With the growing number of electric vehicle (EV) users and the expanding EV industry, and considering the potential of EVs as flexible load storage resources, this paper proposes a post-disaster power supply restoration strategy that takes into account the potential of coordinated scheduling of EVs and mobile energy storage. First, a compression method based on the Minkowski addition is proposed for the EV cluster model in charging stations, which establishes an EV dispatchable model. Second, the spatiotemporal matrix of failure rates for distribution network elements is calculated using the Batts wind field model, enabling the generation of distribution network failure scenarios under typhoon conditions. Finally, the power supply restoration strategy of multi-source coordination with the participation of EV cluster and mobile storage is formulated with the objective of minimizing the loss of the distribution network side. Simulation results demonstrate that the proposed strategy effectively utilizes the load storage potential of EVs and mobile energy storage, enhances recovery performance, ensures cost-effectiveness, and explicitly solves the islanding operation stability problem. Full article

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2. Full article

To investigate the pneumatic characteristics of a piston-type air compressor during the rapid transient processes of intake and compression, this study establishes a computational model incorporating the tank, valves, cylinder, intake and discharge pipe, etc. Utilizing the dynamic mesh method combined with user-defined functions, numerical calculations were performed to analyze the compression process, focusing on pressure variation patterns at various positions inside the cylinder and their impact on compressor performance. The purpose is to enhance understanding of these dynamics. Key findings reveal that during the intake phase, pressure at all monitored points rapidly decreases, with the most significant pressure changes occurring directly below the intake valve. Pressure variations on the surfaces of the intake and discharge valves exhibit high consistency. However, during compression, negative pressure changes become more pronounced. The pressures on the top, side walls, and bottom of the cylinder rapidly decrease as the compression ends. Furthermore, as air flows into the storage tank, its pressure decreases but remains mostly stable until equilibrium is reached, causing the tank pressure to rise. Finally, significant low-pressure areas were observed in small corners below the pipe, while higher pressure values were found in larger corners above the side, demonstrating flow characteristics and energy loss under different geometric conditions. Full article

With the increasing proportion of Distributed Generation (DG) in distribution networks and growing electricity business expansion demand, the integration of DG and new loads imposes significant impacts on distribution networks. To address the incomplete evaluation of active distribution network acceptance capacity, this paper proposes a multi-modal load acceptance capacity assessment methodology incorporating load growth patterns while comprehensively analyzing DG integration impacts. Firstly, differentiated load dynamic models are established to reveal the spatiotemporal distribution characteristics of multi-type loads. Secondly, a load growth model is presented based on spatiotemporal probability decomposition, accompanied by a multi-constraint acceptance capacity evaluation index system tailored to distribution networks. Moreover, an improved repetitive power flow method is developed, and a proposed acceptance capacity evaluation model is proposed to achieve the comprehensive evaluation of multi-type load acceptance capacity in active distribution networks. Finally, the effectiveness of the proposed acceptance capacity evaluation model is proven by a case study of an IEEE 33-node system, and multidimensional analysis is also conducted to investigate the impacts of DG type, DG installation location, DG proportion, and user-side energy storage system (ESS) access on the distribution network’s load acceptance capacity. Full article

With the rapid growth of renewable energy integration, battery energy storage technologies are playing an increasingly pivotal role in modern power systems. Among these, electric vehicle distributed energy storage systems (EV-DESSs) using vehicle-to-grid technology and commercial battery energy storage systems (BESSs) exhibit substantial potential for user-side energy storage applications. A comparative analysis of the cost competitiveness between these two types of energy storage systems is crucial for understanding their roles in the evolving power system. However, existing studies lack a unified framework for techno-economic comparisons between EV-DESSs and commercial BESSs. To address this research gap, we conduct a comprehensive, technology-rich techno-economic assessment of EV-DESSs and commercial BESSs, comparing their economic feasibility across various grid services. Based on the technical modeling, this research simulates the operational processes and the additional battery degradation of EV-DESSs and commercial BESSs for providing frequency regulation as well as peak shaving and valley filling services. Building on this foundation, the study evaluates the cost competitiveness and profitability of both technologies. The results indicate that the levelized cost of storage (LCOS) of EV-DESSs and commercial BESSs ranges from 0.057 to 0.326 USD/kWh and from 0.123 to 0.350 USD/kWh, respectively, suggesting significant overlap and thus intense competition. The benefit–cost ratio of EV-DESSs and commercial BESSs ranges from 26.3% to 270.1% and from 19.3% to 138.0%, respectively. Battery cost and cycle life are identified as the key factors enabling EV-DESSs to outperform commercial BESSs. This drives a strong preference for lithium iron phosphate (LFP) batteries in V2G applications, allowing for LCOS reductions of up to 4.2%–76.3% compared to commercial BESSs across different grid services. In contrast, ternary lithium-ion batteries exhibit weaker cost competitiveness in EV-DESSs compared to commercial BESSs. While solid-state and sodium–ion batteries are promising alternatives, they are less competitive in V2G applications due to higher costs or a shorter cycle life. These findings highlight the superiority of LFP batteries in current V2G applications and the need to align cost, cycle life, and safety performance in the development of next-generation battery chemistries. Full article

Orderly power consumption is an important method for maintaining the supply–demand balance in the power system. However, the large-scale integration of renewable energy significantly raises demand-side load flexibility requirements, challenging the implementation of orderly power utilization. The optimal configuration and scheduling of distributed energy resources (DER), including electric vehicles (EVs) and energy storage systems (ESS), represent promising approaches to addressing this issue. However, current research neglects the influence of DER configuration schemes on the participation rate of EV users in orderly power utilization. This work proposes an optimized configuration strategy for PV and ESS to enhance the participation rate of EV users in grid regulation. An economic configuration model of PV and ESS is constructed to obtain the optimal configuration plan. An incentive pricing strategy based on the configuration plan is proposed to improve the participation rate of EV users in orderly power scheduling. Simulation results demonstrate the effectiveness of the proposed configuration strategy. Full article

The strong uncertainty of the high proportion of new energy and the gradual decrease in the proportion of thermoelectric units have led to a shortage of system flexibility resources. System-level energy storage can efficiently alleviate the pressure of peak shaving and frequency regulation. Effective aggregation of flexibility resources is a key technical foundation for enhancing economic operation and advanced user-side response strategies of new power systems. However, the decentralization and heterogeneity of flexibility resources across generation, grid, load, and storage sides pose dual challenges of aggregation speed and accuracy. In view of this, this paper proposes a large-scale multi-dimensional flexibility polymerization method based on different response time scales. First, the flexibility resource definitions and response characteristics of generation, grid, load, and storage sides were analyzed and categorized according to their response time scales. Second, flexibility regulation models for resources on each side were established. On this basis, an improved Minkowski aggregation algorithm is proposed to precisely quantify the regulation capabilities of multi-dimensional flexibility resources at different time scales, enabling efficient resource aggregation. Finally, the results of the case analysis show that the proposed method can accurately aggregate the flexibility resource adjustment capabilities at different time scales to respond to the multi-time scale flexibility requirements of the system. Full article

To enhance the utilization of emerging energy sources, the application of battery energy storage systems (BESSs) was increasingly explored by investors. However, the immature development of BESS technologies introduced supply–demand imbalances, complicating the establishment of standardized cost analysis frameworks for potential investments. To address this challenge, a hybrid optimization model for a user-side BESS was developed to maximize total net returns over the system’s entire life cycle. The model accounted for factors such as energy storage arbitrage revenue, government tariff subsidies, reductions in electricity transmission fees, delays in grid upgrades, and overall life cycle costs. Conditional value-at-risk (CVaR) was employed as a risk assessment metric to provide investment allocation recommendations across various risk scenarios. An example analysis was conducted to allocate and evaluate the net returns of different battery types. The results demonstrated that the model identified optimal investment strategies aligned with investors’ risk preferences, enabling informed decision-making that balanced returns with operational stability. This approach enhanced the resilience and economic viability of user-side energy storage configurations. Full article

The optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach to include the demand side in the design and operation optimization of the energy system of an Energy Community. The goal is to evaluate the economic, energetic, and environmental benefits when users with different demands are aggregated, and different degrees of flexibility of their electricity demand are considered. The optimization is based on a Mixed-Integer Linear Programming approach and is solved multiple times by varying (i) the share of each type of user (residential, commercial, and office), (ii) the allowed variation of the hourly electricity demand, and (iii) the maximum permitted CO₂ emissions. Results show that an hourly flexibility of up to 50% in electricity demand reduces the overall system cost and the amount of energy withdrawn from the grid by up to 25% and 31%, respectively, compared to a non-flexible system. Moreover, the aggregation of users whose demands match well with electricity generation from renewable sources can reduce CO₂ emissions by up to 30%. Full article

With the rapid development of DC power supply technology, the operation, maintenance, and fault detection of DC power supply equipment and devices on the user side have become important tasks in power load management. DC/DC converters, as core components of photovoltaic and energy storage DC systems, have issues with detecting ground faults on the positive and negative input/output buses, leading to difficulties in troubleshooting device malfunctions and potentially endangering user safety. To address these issues, a method for detecting ground faults on the positive and negative buses of a synchronous buck photovoltaic and energy storage DC/DC converter is proposed, which involves the comprehensive measurement of multi-point common-mode voltages. This method collects the input positive bus voltage, output positive bus voltage, switch voltage, and the common-mode voltage at the midpoint of the bridge arm, then sums these after removing the switching harmonics. By analyzing the characteristic differences of the summed voltage under the ground fault modes of the positive and negative input/output buses, characteristic parameters are extracted to establish a ground fault identification method, thereby achieving effective detection of ground faults in the photovoltaic and energy storage DC/DC converter. Finally, the effectiveness of the method proposed in this paper was validated through simulations and experiments. Full article

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side. Full article

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system. Full article

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system. Full article