Sustainable Energy Grids & Networks最新文献

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.

Understanding the ever-changing dynamics of power systems is crucial, and dynamic state estimation (DSE) plays a vital role in achieving this. However, traditional nonlinear Kalman filters (NKFs) face limitations: lack of access to control inputs and presence of non-Gaussian noise in measurements, impacting their accuracy and robustness. This research introduces a novel robust DSE method that tackles these challenges head-on. For the first time in DSE, it leverages the predictive power of Holt-Winters Triple Exponential Smoothing to model the time-varying behavior of control inputs. This innovative approach allows for the simultaneous estimation of dynamic state variables such as the rotor angle and rotor speed changes, as well as transient voltages and control inputs like mechanical input torque and excitation voltage, even in the presence of non-Gaussian noise. Furthermore, the method employs modified projection statistics and a Cauchy function. This unique combination effectively bounds the influence of observation outliers while maintaining high statistical estimation efficiency. This innovative approach utilizes a square cubature Kalman filter (SCKF) for enhanced numerical stability. Extensive simulations under various anomalous conditions demonstrate the method's superior accuracy and efficiency in estimating the state vector. These results highlight its potential to significantly improve power system estimation and pave the way for real-time applications.

The implementation of transactive energy systems in communities requires new control mechanisms for enabling end-use energy trading. To optimize the operation of these communities, the availability of accurate predictions for the net energy demand is fundamental. However, to ensure effective management of flexible resources, the local generation and demand must be foretasted separately instead of just forecasting the net-energy demand. Additionally, to improve the forecast systems, more detailed data from the buildings are needed, but most information (such as patterns of occupancy) can be private. This paper proposes a novel federated learning (FL) framework for predicting building temporal net energy demand in transaction energy communities. The proposed approach is based on an FL architecture and has two independent forecast systems (generation and demand systems), ensuring collaborative learning among the buildings without sharing private data. The developed framework allows the integration of third-party data providers and facilitates coordination by a central server. The main goal of the framework is to support the management systems of transactive energy communities by computing the forecast of demand, generation, and net-energy demand. Additionally, such a framework has the novelty of introducing as an auxiliary system of Federated Transfer Learning, which will guarantee a more capable forecast system for new communities. The developed structure was tested using two communities, one with 100 buildings and the second with 25. The results showcase high accuracy and adaptability to different variables and scenarios, for instance, seasonal variations.

The increasing utilization of distributed generation resources led to the formation of active distribution networks and virtual power plants (VPPs), which have changed the paradigms of electrical energy transactions in local energy markets. The VPPs can form capacity-withholding groups and impose market power to gain more profits, which may increase the costs of energy procurements for consumers. This paper presents an algorithm for the local electricity market operator in distribution networks to assess the dynamic capacity withholding of VPPs in the local energy and reserve markets. The main contribution of this paper is proposing indices to evaluate the dynamic capacity withholding of VPPs in energy and reserve markets. The other contribution of this paper is that it also quantitatively analyzes the impact of withholding processes on the flexibility of the distribution network. An optimization process is used to estimate coordinated offers of VPPs in the energy market in order to prevent the formation of withholding groups. The proposed algorithm was assessed for the 123-bus IEEE test system and the energy and reserve dynamic capacity-withholding indices were determined for different operating conditions.

This paper proposes a multi-objective optimization framework to model the energy trading between microgrids and microgrid communities in the distribution systems. To this end, a hybrid cooperative and non-cooperative algorithm is presented where the microgrid community leads the optimization problem. The microgrid community performs a multi-objective optimization to determine the transactive retail prices to simultaneously improve its operation cost and system flexibility. However, the microgrids, as the followers of the problem, receive the retail prices from the microgrid community to decide on the amount of hourly trading with the microgrid community. The main objective of microgrids is to reduce their cost as much as possible. For this reason, they cooperate to form several coalitions to enhance their bargaining power in the market. Real-time scheduling will be done to increase the reliability of the proposed model and reduce the imbalance costs of the microgrid community and microgrids. The proposed model is tested on a general case study, and the simulation results show that the cooperation among microgrids reduces their operation costs from $ 3453.66 to $ 2984.33. Also, the multi-objective scheduling increases the flexibility by 28.5 %.

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.

Vigorously developing new energy (NE) is an important measure to deal with energy crisis and environmental deterioration. However, the high proportion of NE connected to the grid in the future will lead to an imbalance between supply and demand for the flexibility of the power system. This study constructs a flexible resource (FR) risk economic balance optimization model. Firstly, a quantitative mathematical model of supply and demand of FR is established. Then, the ladder-type carbon trading mechanism is designed, which reduces the carbon emission of flexible thermal power (FTP) by 553.96 t, or 0.25 %, and reduces the carbon emission cost of ¥546,933.08, or 10.5 %. The carbon emission cost of supply side FRs is allocated to each load. Secondly, conditional value at risk (CVaR) is integrated into the objective function to measure the risk loss caused by insufficient flexibility of the system. Finally, to minimize the total operation costs, we design start-stop plan, output power, and regulation rate for the FTP, energy storage system (ESS), and pumped storage (PS); to maximize the customer satisfaction of electricity consumption, we design the peak-valley time-of-use (TOU) price of shifted load (SL) and cut load (CL), and design the total constraint of demand response (DR). Simulation on a typical day shows that: (1) The proposed model can realize low-carbon optimization of FR while considering both economic and risk, and improve scheduling executability and customer satisfaction of electricity consumption; (2) Different types of FRs can be coupled together to reduce system operation costs and carbon emissions.

Impedance analysis is a practical approach for assessing the small-signal stability of renewable energy power systems. However, existing research predominantly focuses on specific operating conditions, neglecting the fundamental principles governing stability evolution under time-varying operating conditions. This paper presents a methodology to develop the small-signal stability region (SSSR) for grid-connected inverters using the impedance method. A comprehensive stability analysis for grid-connected inverter systems is performed based on the stability region. Firstly, the multi-parameter SSSR of the grid-connected inverter is defined according to both the aggregated impedance criterion and the generalized Nyquist criterion. Furthermore, a polynomial approximation expression for the SSSR boundary is derived. Secondly, the sensitivity analysis of operating points and control parameters is performed under full operating conditions to investigate their impact on stability based on the quantified boundary. The analyses reveal that the stability of the grid-connected inverter system near the SSSR boundary decreases with increasing active power and decreasing reactive power but exhibits an initial increase followed by a decrease with a larger PLL bandwidth. Finally, the accuracy of the stability region and the influence of key parameters are verified through case studies and experiments. The study in this paper can be used for quantitative analysis of stability margins and decision guidance of control optimization for grid-connected inverters.

Traditional distribution system recovery strategies can handle non-overlapping disasters, typically a single N-k failure, rather than multi-overlapping disasters. Multi-overlapping disaster refers to a scenario in which a system experiences multiple N-k failures, with a new N-k failure occurring before the system has been fully restored from the previous one. To address the recovery problem under multi-overlapping disasters, a rolling recovery model for unbalanced distribution systems that considers both repair crews (RCs) and mobile power sources (MPSs) is proposed. The proposed rolling recovery model can automatically optimize preceding recovery strategies based on the grid topology and the state of each resilient resource at the overlapping moment of each disaster. Case studies are conducted on the modified IEEE 33-node test system to demonstrate the concept of multi-overlapping disaster recovery. Compared to traditional methods that treat multi-overlapping disasters as multiple individual disasters, the case studies demonstrate that the proposed model can reduce load shedding by about 6.91 %, which verifies the effectiveness of the proposed methodology for updating recovery strategies at overlapping moments of disasters.

With the recent transition to a low-carbon electrical power system (EPS), the large-scale utilization of renewable energy resources in electrical power generation introduces a substantial amount of uncertainty on the generation side of the EPS. This uncertainty, along with the inherent uncertainty of electricity demand, makes assessing generation reliability a very computationally intensive process. To enhance the computation efficiency of EPS generation reliability assessment, it is crucial to have an efficient probabilistic model of available generation capacities that strikes a balance between improved computational performance and model accuracy. In this paper, various probabilistic models are proposed to characterize the variability and uncertainty of conventional and renewable power generations (photovoltaic and wind). On the basis of these models, an analytical formulation of probabilistic reliability indices (RIs) is implemented. The computation time and accurate RIs values found using the Monte Carlo simulation method serve as the basis for reporting solving time improvements with corresponding losses in the accuracy of the RIs for different analytical methodologies. The results of multiple case studies of an EPS are presented, considering various combinations of conventional and renewable generation capacity, levels of renewable power penetration, and system reliability levels. The results indicate the practical implementation of analytical assessment methodologies compared to the simulation method in terms of accuracy and computational effort. This study is of immediate relevance and potential importance to operational reliability and generation expansion planning studies in EPSs.