Abstract:In order to reduce the risk brought by the uncertainty of renewable energy output and electric vehicle（EV）travel to virtual power plant（VPP）scheduling, an optimal scheduling model of virtual power plant incorporating electric vehicles based on information gap decision theory（IGDT）is proposed. Firstly, a Monte Carlo load prediction model is established based on the behavior characteristics of private EV users, and the Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive price.Secondly, based on the VPP framework, wind power, photovoltaic power, gas turbines, energy storage systems and EV clusters with vehicle to grid（V2G）capabilities are integrated to establish an economic optimization scheduling model considering multisource collaboration. Then, aiming at the uncertain parameters in the model, the information gap decision theory is introduced, and a twolevel decision-making mechanism with both risk aversion and opportunity seeking is constructed. Finally, a virtual power plant is tested with an example to verify the correctness and effectiveness of the proposed model and algorithm. The results show that the method can realize the load side peak balancing and valley filling effectively, and has advantages in economy and stability.

Abstract:Agricultural greenhouses, as the core component of modern facility agriculture, exhibit significant demand response potential due to their complex energy consumption characteristics. A greenhouse power demand response strategy based on crop growth safety constraints has been proposed. Firstly, the types of loads within the greenhouse are categorized, distinguishing between shiftable and interruptible loads. Then, by characterizing the nonlinear relationship between environmental parameters and electricity consumption, an electricity load model required for crop growth is established, and a greenhouse power demand response model is constructed with the objective of minimizing operating costs. To address the issue of traditional black hole algorithms easily falling into local optima, an adaptive crossover mutation mechanism and dynamic inertia weight strategy are introduced to develop an improved black hole optimization algorithm. Simulation results using a typical tomato greenhouse as a case study demonstrate that the proposed method reduces daily load and electricity purchase costs while ensuring normal crop growth. The research results provide an economical solution for demand-side response in the agrcultural greenhouse sector.

Abstract:A hybrid wind speed forecasting model based on discrete wavelet transform（DWT）and bidirectional recurrent neural networks to address the prediction challenges caused by the non-stationary characteristics of wind speed data is proposed. The model employs a three-stage architecture：first, DWT decomposes the non-stationary wind speed sequences into multiple frequency sub-bands to extract multi-scale features；second, each sub-band is fed into bidirectional long short-term memory networks（BiLSTM）and bidirectional gated recurrent units（BiGRU）for parallel processing to fully capture long-term and short-term temporal dependencies；finally, a meta-learner intelligently fuses all sub-model predictions to generate the final wind speed forecast. Experiments on real data from the Sotaventogalicia wind farm in Spain demonstrate that the proposed model significantly outperforms traditional methods and existing DWT-based models across all evaluation metrics. The DM statistical test confirms the statistical significance of the performance improvement, indicating that this hybrid model provides a high-accuracy solution for wind speed forecasting.

Abstract:As the agricultural load is greatly affected by meteorological factors and a single decomposition method cannot fully extract the multidimensional features existing between multiple inputs, an agricultural load forecasting model based on multivariate variational mode decomposition combined with SVR Bi GRU TCN combined model is proposed. Firstly, using multivariate variational mode decomposition to adaptively decompose historical agricultural loads and meteorological characteristics, real-time mining of modal components with different feature scales between data is carried out. Then, based on the inherent properties of each modal component, SVR, Bi-GRU, and TCN models are established to extract feature information at different time scales, thereby achieving accurate prediction of future 1-hour agricultural loads. The experimental results show that compared with the SVR model, Bi-GRU model, and TCN model, LSTM model and CNN-BiLSTM model, the proposed prediction model can effectively improve the prediction accuracy.

Abstract:To promote the high-proportion consumption of distributed photovoltaic（PV）power and drive the implementation of large-scale power substitution, an optimized scheduling plan based on source-load coordination for agricultural parks has been proposed. Firstly, various types of equipment in the planting and breeding scenarios in the park are modelled in great details and the potential, characteristics,and flexibility of agricultural electrified loads to participate in load regulation are analyzed and clustered. Secondly, a multi-objective optimization model that takes into the consumption rate of distributed PV energy and the operational cost of the equipment are taken into account, as the constraints are on the operation of the equipment and the energy demand of agricultural production. The objective is to carry out coordinated scheduling of the integrated energy system in the park. Finally, a simulation analysis is conducted in a high percentage electricity substitution agricultural park in South China as an example, and the results verify the effectiveness of the proposed method in improving the PV consumption rate, reducing the park’s operating costs, and promoting large-scale electricity substitution.

Abstract:The topology of low-voltage distribution grids（LVDGs）depicts how various electrical components are physically interconnected within the distribution system. Due to the use of distributed energy resources（DERs）, there is an overlooked mutual load dependent characteristics among end users, which brings great challenges to node correlation analysis and topology identification. In this regard, a low-voltage topology identification method focusing on the mutual load dependent characteristics of DERs is proposed. First, a user classification method based on support vector machineis proposed to classify users according to usage of DERs at different times. Then, convolutional recurrent neural network is applied for distributed feature extraction among load data to decrease load dependency. Finally, the sibling pair search algorithm with residuals resistance is proposed to hierarchically identify the topology. Test results in different simulation scenarios and practical LVDGs demonstrate the effectiveness and robustness of the proposed method.

Abstract:A collaborative control strategy for domestic air conditioners with adaptive access is proposed addressing communication barriers caused by diverse and incompatible device protocols in heterogeneous domestic air conditioners, along with low computational efficiency in real-time regulation. First, the domestic air conditioner information interaction architecture is constructed, and the adaptive access method is proposed on this basis. Then, the deep reinforcement learning multi-conditioner collaborative control strategy is developed, and the soft-max sampling strategy and the prioritized experience replay mechanism are introduced to improve the MAD3QN algorithm, and the SMPER-MAD3QN algorithm is proposed. Finally, a centralized training with decentralized execution is implemented based on SMPERMAD3QN, which allows multiple air conditioners to collaboratively participate in the regulation of the algorithm. The simulation results measured that the packet loss rate of multi-protocol domestic air conditioner information interaction is 0.36%, and the interaction latency is lower than 25ms, which indicates that the adaptive access can significantly shorten the real-time decision-making time and realize the unified management and control of multi-protocol domestic air conditioner. Meanwhile, the proposed algorithm realizes the collaborative participation of multiple air conditioners in demand response（DR）under the premise of guaranteeing the comfort of users, and the algorithm has excellent robustness, which improves the flexibility and reliability of the dispatchable resources on the demand side.

Abstract:In order to solve the problem of reliability and flexibility of new power system with uncertainty on both sides of the source and load, taking reserve market as research object, considering coordination and complementarity characteristics of multiple types of energy sources in source-network-load-storage and impact of electricity price on reserve cost during peak and valley periods, a time series double layer reserve optimization strategy is proposed that considers the response prioritization of source-network-load-storage multi-standby with standby operating costs, response time, and carbon emissions as optimization objectives. Firstly, operation characteristics of each reserve body of source- network-load-storage are analyzed and mathematical models are established. Then, a two-layer optimization model for source-network-load-storage multi-reserve coordination with multiple optimization objectives is built. Hierarchical analysis is used in the inner layer to compute the temporal response order factor of unit-capacity multi-reserve under different optimization objective ratios. The optimal ratio of the multi-optimization objective is searched and the standby capacity optimal allocation of source-network-load-storage multi-reserve for different time periods. Finally, Power grid in a province is used as an experimental example for simulation verification,and the results show that the proposed method can effectively reduce reserve cost, response time and carbon emissions.

Abstract:To address the imperfect bidding mechanisms for independent energy storage as an emerging market participant in competitive joint electricity markets, the bidding behavior of various market entities by modeling power quantities and price offers is simulated. Based on this, a bi-level optimization model is constructed to represent the bidding strategy of independent energy storage participating in the day-ahead energy and reserve joint market. In the upper level, the independent energy storage system maximizes its revenue in the dayahead energy and reserve joint market by optimizing its power-price bidding strategy. In the lower level, the power trading center clears the market by minimizing the total social electricity procurement cost. To improve the computational efficiency of the model, the lower-level problem is reformulated using the KKT conditions and the strong duality theorem. Simulation studies are conducted using a commercial solver to verify the effectiveness of the proposed bidding strategy in optimizing independent energy storage participation and enhancing its performance in the market.

Abstract:In recent years, distributed photovoltaic（PV）systems have entered a period of rapid development. Affected by the site environment, the distributed PV system is vulnerable to the nearly shadows during operation, resulting in loss of power generation which affecting demand side energy management. The generation performance of distributed PV system under the influence of nearly shadow is simulated and analyzed. The influence mechanism of shadow on the output characteristics of PV array is studied, the loss of power generation due to shadow is quantified, and the PV array engineering model and occlusion model are established. Based on this, combined with the annual solar position algorithm, the annual power generation of a distributed PV system under shadow is simulated, and the power generation loss and the economic loss of the distributed PV system before and after shadow is analyzed. The results can provide a reference for the design and planning of distributed PV system.

Abstract:Aiming at the challenges of stochastic fluctuation at both source-load terminals in the optimization operation process of integrated building energy system, considering the influence of source-load uncertainty factors, a day-ahead optimization method for integrated building energy system based on fuzzy chance-constrained programming is proposed. Firstly, based on the equipment model of the integrated building energy system, the day-ahead optimal operation model of the integrated building energy system is established. Secondly, considering the influence of the uncertainty of photovoltaic and electricity, cooling and heat loads in the building system, the uncertainty of source-load is represented in the form of fuzzy chance constraint based on fuzzy membership function, and the day-ahead optimization operation model of building integrated energy system based on fuzzy chance constraint programming is proposed, and the clear equivalence class method is used to transform and solve the model. Finally, the effectiveness of the proposed method are verified by an example analysis.

Abstract:The increase of the proportion of distributed energy connected to the grid brings huge threats to the stability and operation cost of the power grid. As common adjustable resource, the electric vehicle（EV）has a huge potential to promote the consumption of renewable energy. In order to solve the coordinated scheduling challenges between the electric vehicles and the distribution network, a multi-time scale optimal scheduling model is designed. Firstly, based on the elasticity model of electricity price, the time-of-use electricity price model on the user’s mental account theory is constructed considering the consumer psychological factors, so as to better guide the charging and discharging of electric vehicles. Secondly, the scheduling is carried separately before and within the day according to whether users sign the incentive agreement, and the optimization is carried out considering the carbon trading mechanism and the master-slave game model. Finally, the mayfly algorithm is improved to improve the solving efficiency. The effectiveness of the proposed model and strategy is proved by Matlab software.

Abstract:To improve wind power accommodation rate and the economy of system operation, a multi-time scale source-grid-load-storage coordinated scheduling strategy that considers the life loss of hybrid energy storage is proposed. Firstly, based on the differential degradation mechanisms of lithium-ion batteries and all-vanadium flow batteries, a battery cycle life loss model accounting for depth of discharge is established to quantify the cost of energy storage life degradation. Secondly, combined with the regulatory characteristics of demand response resources on the load side, through the collaborative optimization of day-ahead scheduling plans and intraday rolling adjustments,an optimal operation model of source-grid-load-storage is built, with the goal of minimizing the total system operating cost and wind curtailment cost. Finally, the results of the modified IEEE-30 bus case study show that the proposed strategy can effectively reduce the cost of energy storage life loss, improve the wind power accommodation rate, and significantly decrease the total system operating cost.

Abstract:There is a huge potential in building energy management. To solve the problem, a PID-Lagrange-SAC algorithm-based regulation method is proposed. Firstly, the problem statement of regulating building energy consumption behavior is modeled as a markov decision process（MDP）model. The state of controllable devices and external variables which introduce uncertainties are established as the state space, and the operating power of controllable devices is used as the decision variable to form the action space. Then, proper reward functions are designed to instruct the agent to learn better regulating strategies. The problem is further extended to a constrained markov decision process（CMDP）, and the Soft actor-critic algorithm is employed to train the agent while PID control and Lagrange method are applied to suppress the behavior of agents violating constraint conditions. The case study shows that the regulating strategy reduces the operating costs and carbon emissions of the building while meeting users’comfort demand, demonstrating the effectiveness and superiority of the proposed method.

Abstract:With the large-scale grid connection of power electronic power sources, the new power system（NPS）gradually exhibits the characteristics of low inertia, and the indexes of frequency characteristics are getting closer to the safety critical value, which seriously affects the frequency safety of system operation. To quantitatively analyze the minimum inertia requirement of the power electronic power system under the condition of multi-resource participation in frequency regulation（FR）when it is disturbed by active power, based on the improved frequency response model of the multi-machine system, a minimum inertia estimation method of the power system considering frequency response characteristics is proposed. The theoretical inertia of each FR unit is represented in the form of rotor kinetic energy,and the calculated inertia of the power system is quantified based on the rate of change of frequency（RoCoF）. The sliding window technique is used to select the data set with the smallest variance and obtain the final calculated inertia of the system. The proposed estimation method takes the initial RoCoF, the maximum frequency deviation and the steady-state frequency deviation as the frequency change constraint indicators, and the improvement measures when the system inertia is insufficient are added to calculate the minimum inertia of the NPS under the multi-resource participation in FR. Finally, the feasibility of the NPS minimum inertia demand estimation method proposed is verified by using simulation software, which provides a guiding basis for the rationalized allocation of NPS FR resources.

Abstract:Microgrids as an important form of distributed renewable energy integration, have garnered significant attention due to their flexibility and efficiency. the fundamental concepts, functional characteristics, and developmental trends of microgrids are revieved systematically. Key operational control strategies for grid-connected, islanded, and transition modes are analyzed in detail, with a focus on the critical role of energy management systems in achieving stable operation, economic scheduling, and energy optimization. In grid- connected mode, optimization models are developed to enhance renewable energy utilization and absorption efficiency, while in islanded mode, centralized and distributed control strategies work collaboratively to maintain system frequency and voltage stability. By integrating existing technical standards, the main challenges of microgrid operation control are identified. Future development suggestions are proposed in the areas of standardization, management modes, and economic improvements, providing valuable insights for the further advancement of microgrid technologies.

Abstract:The accurate prediction of spot market electricity prices plays a crucial role in protecting the interests of participants in the electricity market. Both raw material prices and climate factors can affect the fluctuation of electricity prices in the spot market. In addition, a large amount of wind and solar energy is currently involved in spot market transactions, making electricity price forecasting in the spot market more challenging. Therefore, a spot market electricity price prediction model that integrates CEEMDAN, BERT, and LSTM is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original electricity price data；Subsequently, the BERT algorithm is used to process the text data of three exogenous features：raw material prices, climate conditions, and renewable energy, in order to improve the prediction accuracy of the model；Next, the electricity price decomposition subsequence is combined with the results of exogenous feature processing, and LSTM is used to predict the model. The predicted results are then overlaid to obtain the final electricity price. Finally, the effectiveness of the proposed method was verified through simulation, and the results show that the CEEMDAN-BERT-LSTM prediction model improved the accuracy of electricity price prediction significantly.

Abstract:In order to address the stability and flexibility challenges posed by continuously increasing proportion of renewable energy in power systems, the German electricity market has clarified market rules in legal form, stimulating the enthusiasm of market subjects represented by virtual power plants to participate in the market. The major energy and power laws and regulations in Germany are outlined firstly. Then, the promoting role of these energy and power laws and regulations in the development of virtual power plants are discussed from three aspects：resource integration, market environment, and sustainable development. Taking the largest virtual power plant in Germany,Next Kraftwerke, as an example, it explores how energy and power regulations create favorable conditions for the emergence and development of virtual power plants. Finally, the reference significance for China is discussed.