With the growth in the electricity market (EM) share of photovoltaic energy storage systems (PVSS), these systems encounter several challenges in the bidding process, such as the uncertainty involved in photovoltaics, limited bidding ability, and single-revenue structure, which significantly impact the market revenue. To address this research gap, a two-stage bidding strategy based on a non-cooperative game is proposed for PVSS to participate in energy and regulation markets. Considering the complexity of the PV output from adjacent multi-PVSSs, a scenario generation method considering spatiotemporal correlation is proposed. Furthermore, a two-stage bidding strategy is constructed, which includes a bi-level offer price model for the day-ahead (DA) market and a bi-level offer capacity model in the intraday (ID) market. In the DA stage, this study balances the interests of the PVSS and market-clearing costs and considers offer prices in the transaction process. In the ID stage, the market balance cost is considered to further increase the revenue of the PVSS. Moreover, the multi-PVSSs ‘competition relationship is analyzed to coordinate market revenues based on a non-cooperative game. The PVSS adjusts its offer by considering that other PVSSs plans to achieve the Nash equilibrium. The superiority of the proposed strategy was validated using an improved IEEE30 nodes system. Compared to the DA stage bidding strategy, adopting the two-stage bidding strategy can increase the revenue of the PVSS by 5.608%. Specifically, the proposed bidding strategy can increase the revenue of the PVSS by 4.993% compared with a cooperative bidding mode.

Photovoltaic output and load uncertainty are prone to cause voltage fluctuations in a distribution network, which can affect the power quality. With a particular focus on the distribution network voltage overrun caused by this problem, we propose a two-stage multi-mode voltage control strategy for distribution networks based on deep reinforcement learning to mitigate voltage fluctuations while realizing coordinated operation among different devices. First, the on-load regulator transformer and capacitor bank are effectively controlled using a centralized control strategy in the day-ahead phase to obtain the optimal tidal current for the distribution network, in order to achieve optimal operation of the long time-scale equipment of the system. Then, a distributed control strategy based on deep reinforcement learning with multiple intelligences is applied to regulate the reactive power of the photovoltaic inverters, based on local observations in the intraday phase, to provide fast, flexible, and reactive power support for the distribution network. Finally, a multi-mode data-driven conversion strategy based on deep reinforcement learning is used to coordinate and convert between different control modes, to enable the system to effectively reduce voltage offsets and reactive power losses under varying operational demand. The proposed method is experimentally validated on an improved IEEE 33-node distribution system, and the results show that it can effectively solve the voltage overrun problem and reduce the reactive power loss in the distribution system.

With the increasing demand of users for distributed energy storage (ES) resources and the emerging development of peer to peer (P2P) transaction technology, shared energy storage (SES) has great potential to contribute into new business models of demand-side ES. In order to compromise essential elements like safety, stability and efficiency of P2P trading, as well as to improve the utilization rate of demand-side ES, this paper devotes to construct a P2P transaction framework based on a partially decentralized topology and proposes a two-stage trading optimization strategy of SES in a P2P market, considering the equilibrium state of supply and demand flow. In the first stage, this paper simultaneously balances the interests of buyers and sellers and brings the carbon trading mechanism into the transaction process. The interaction of interests of bilateral parties with consideration of carbon trading mechanism has been investigated, and a SES capacity sharing model is, then, established based on the bargaining game theory. In the second stage, a unique pricing mechanism for SES leasing fee is designed based on a multi-strategy evolutionary game model, considering bounded rational decision-making for SES operators and communities. Finally, numerical simulation verified the feasibility and superiority of the proposed P2P trading strategy of SES.

Accurate estimation of the State of Health (SOH) for lithium-ion batteries is necessary for the stable operation of the battery system. To accurately estimate the SOH for lithium-ion batteries, we propose an SOH estimation method based on the features of the variation coefficient of partial charging curves, feature processing, and Gaussian Process Regression (GPR). Firstly, the features of the variation coefficient are extracted from the partial charging voltage and current curves as health indicators. The extracted features are efficient and practical, and can effectively reflect the aging phenomenon of batteries. Subsequently, to suppress existing noises, Box-Cox transform (BCT) and discrete wavelet packet transform (DWPT) are employed for the extracted feature signals, thus improving the correlation between the features and the SOH, and ensuring the reliability of the overall framework. Moreover, aiming at the parameters selection problem of the GPR model, an improved particle swarm optimization algorithm with mutation factor and self-adaptive weight adjustment according to population diversity is introduced. Finally, the proposed SOH estimation framework is verified on the NASA battery data set. The experimental results show that the estimation error of the proposed model can be kept within 1.5 % based on different training sample sizes. The results show that the proposed model has high estimation accuracy, generalization, and adaptability.

AbstractThere are significant differences in distributed generators (DGs) distribution and load characteristics between different voltage levels, which makes it difficult to match sources and loads. We focus on the problem of different consumption capacities at different voltage levels and the divergence of interests among investment entities, and propose a coordinated planning model for DGs and soft open points (SOPs) based on the Stackelberg game. Firstly, a model of a multi‐voltage level distribution networks (DNs) is constructed based on SOPs. Next, the source‐load matching degree is proposed as a measure of the degree of matching between sources and loads in the DN, and the source‐load consumption rate is selected as an indicator to evaluate the impact of the load on DG consumption. Following this, the interest demands of DG investors and distribution company (DisCo) in multi‐voltage levels DN are analyzed, a planning mode based on the Stackelberg game is proposed, and this is solved by combining the genetic algorithm with second‐order cone programming. Finally, the effectiveness of the planning model is tested and verified using an improved IEEE 28‐node system. The results show that the proposed model improves the DG consumption capacity of DNs with multiple voltage levels while protecting the interests of DG investors and DisCo.

To overcome the difficulty in tracking the operation state of distribution networks (DNs) when the specific distribution of system noise and measurement errors is unknown and the measurement is insufficient, a dynamic state estimation (DSE) method based on adaptive set membership filter (SMF) is proposed in this article. First, for the sampling period and measurement delay differences of various measurements, a multisource data fusion strategy was proposed to achieve the synchronization of measurement data at the sampling moment. Subsequently, considering unknown but bounded (UBB) noise, an ellipsoid-based DSE model was established, which unified the form of multisource data through measurement transformation strategies and linearized the measurement function. Then, an adaptive SMF considering bad data detection was proposed to solve the proposed DSE model. The state variables at different moments were iteratively solved through three steps: time update, bad data adaptive detection, and measurement update. Finally, the effectiveness and robustness of the proposed method were verified based on the IEEE33-bus distribution system, the 118-bus test system, and the 34-bus real test system.

To improve the comprehensive utilization efficiency of energy, a multi-objective optimization control strategy applied to the energy hub (EH) within the system is proposed to address the electrical and thermal load distribution of the integrated energy system (IES) and the low-carbon economic operation. First, a model of the electrical and thermal energy outputs is established based on the characteristics of the IES network and the multidimensional "load parameter" evolution law. Moreover, a distributed control strategy is proposed that utilizes the information interaction between neighboring EHs to accurately share the electrical and thermal loads, which reduces the communication burden of the system while avoiding overloads. Furthermore, to coordinate the optimal operation of the devices within the hub, based on the energy conversion characteristics of the EH, a multi-objective optimization model is proposed that considers low-carbon and economic aspects to realize efficient energy use. Simulation results show that the proposed strategy effectively improves the robustness of the system while realizing proportional load distribution and low-carbon economic operation. Under the same load, when focusing on system economics, the operating cost is 2.182/& YEN; & YEN; lower than when focusing on low-carbon systems, but carbon emission is 1.6753/kg CO2 2 higher.

Power prediction can effectively mitigate the uncertainty in photovoltaic power generation, enabling better operation and scheduling of power grids. Therefore, in this study, a multi-step interval prediction method for ultra-short-term photovoltaic power from time-series-segment (TSS) feature analysis is proposed. First, three TSS indicators are defined to determine the fluctuation characteristics of historical data and combined with fuzzy C-means clustering to address the time mismatch problem. Subsequently, a deterministic multi-step prediction method is proposed based on the optimal membership search using deep recurrent neural networks, improving the prediction stability. Finally, based on the difference in the TSS types, an improved interval prediction method is proposed in combination with Gaussian process regression, narrowing the average interval width. Experiments are conducted to compare the performances of the conventional and proposed methods using measured data from Australia. Compared with the baseline scheme, the proposed scheme enhances the accuracy of multi-step prediction by 19.7%, and the average error of each step does not exceed 5%.The average interval width is reduced by 45.6% while ensuring more than 95% interval coverage in the probability interval prediction. The experimental findings demonstrate that the TSS feature analysis can effectively reveal the potential patterns of PV power output under various weather conditions. This enables the algorithm to learn clearer sample features and thus enhances the performance of multi-step probability interval prediction.

In the planning and operation of power systems containing wind power, it is of great significance to use a small number of representative wind power time series scenarios to accurately portray the stochastic characteristics of wind power. With the increase of the number of scenarios, how to form representative typical scenarios to balance computational efficiency and accuracy is an urgent problem to be solved. In this study, a bi-directional optimization method is proposed to generate daily wind power time series scenarios based on a single-period optimal scenario generation strategy and a multi-period scenario reduction strategy. First, the Wasserstein probability distance index is used to form a scenario model that is best approximated to the probability distribution of wind power, and the optimal scenarios of each period are generated. Secondly, a wind power scenario reduction strategy that integrates spatial distance and stochastic features is proposed. The improved taboo search method is used to selectively connect the representative scenarios of each period to form the representative daily wind power time series scenarios. Finally, the effectiveness and practicality of the proposed wind power time series scenario generation method are verified by simulations.

With the increasing penetration of distributed photovoltaic in distribution network, it is more difficult to control active distribution network (ADN). A flexible interconnection device (FID) can realize regional interconnection of the ADN through transferring power. However, the influence of installation position and number of FIDs on the ADN varies, it is necessary to analyze its operational planning model. In this study, first, a photovoltaic power and load forecasting model is established, followed by reduction of the typical scenarios. Second, the ADN operational planning model is established according to the line load balance index to analyze the coordination and determine the power transfer value of the FID in different installation positions and numbers for the ADN. Third, the net income index of the distribution network in the entire life cycle is established to analyze the economy of the ADN and determine the optimal FID installation location and quantity. Finally, numerous simulations and comparisons are carried out on the actual example system, and the results show that the FID can effectively transfer the active power, strengthen the ADN power supply level, and solve the problem of power supply imbalance caused by distributed photovoltaic access.

Currently, the lack of regulation ability in distribution systems causes severe limitations in the wide grid connection of high-penetration renewable distributed generation (DG). By improving the access of SOPs, it will considerably improve the economy, flexibility, and controllability of distribution system operation. A bi-level coordinated planning model of DG and soft open points (SOPs) in an active distribution network is proposed based on a complete information dynamic game to coordinate the interests and demands of DG investors, distribution companies, and electricity consumers, and flexibly adjust the power between feeders. The model's planning stage determines the sequential decision relationship from the perspective of improving the consumption of DG and providing economic value. It analyzes the dynamic game behavior and builds the planning model to determine the optimal installation sites and capacities of DG and SOPs for different stakeholders. At the optimization stage, the operation model of the distribution network is established to reduce the network loss and voltage deviation and balance the feeder load. A hybrid optimization method is used to solve the operation-planning model using the iterative search method and mixed-integer second-order cone programming. Furthermore, numerous simulations and comparisons are carried out on IEEE 33-node system, and the results show that the proposed method can not only meet the balance of interests of different stakeholders, improve the feasibility and economy of planning schemes, but also comprehensively improve the operation state of distribution system.