Sustainable Energy Grids & Networks最新文献

The distribution area of the district heating network (DHN) is extensive, and there are inherent time delays and thermal losses in the process of heat transfer through heating pipes. The delay in heat transfer within long-distance heating pipes may result in inadequate heat supply to end-users or excessive energy consumption at the heat source. Therefore, this paper presents a quasi-dynamic model for calculating the transmission delay time in the long-distance heating pipeline. And the model is validated through the measured values obtained from a heating pipeline. The influencing factors of delay time are further discussed, including operating parameters, pipe structure parameters and thermal insulator thickness. Additionally, the impact of pipe delay time in practical engineering is analyzed. In practical engineering, the transmission delay time varies when the pipe structural or operational parameters differ, even under the same outdoor temperature change. The change in inlet water temperature and mass flow rate can impact the change rate of outlet water temperature, thereby influencing the delay time. Furthermore, the delay time exhibited an increase with pipe length, diameter, and thermal insulator thickness; however, the effect of thermal insulator thickness on it was minimal. When the inlet water temperature rose or dropped by 5℃, the delay time grew by more 70 % per 1 km pipe length, about 40 % per 100 mm diameter and less 2 % per 100 mm thermal insulator thickness, respectively.

The cyber security of power system state estimation (PSSE) is crucial, and its robustness against evolving false data injection attacks (FDIA) is being rigorously assessed to develop advanced countermeasures. Existing FDIA methods have achieved satisfactory success rates but often fail to align with practical constraints such as the assumption of partial or complete knowledge of the power system network by the attacker, modifications in generator output measurements, and the sparsity of the attacks. This work proposes a near practical, stealthy approach using a deep generative adversarial network-long short-term memory autoencoder (GAN-LSTMAE) learning based sparse FDIA method against AC PSSE, leveraging only measurement data. To evade the bad data detection (BDD) mechanism effectively, an LSTMAE-based PSSE mimic is proposed, further optimizing the GAN-based attack generator to embed the physical laws of the system along with measurement residuals and temporal dependencies of states to the generated false data. The proposed modified training data preparation algorithm, coupled with the attack sub-graph method, defines the optimal attack region while keeping generator output measurements intact. The generated attack is validated extensively using IEEE 14 and 118 bus test benchmarks against various defense techniques, demonstrating high success rates.

Distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks, which may harm the health of power equipment and increase the operational costs. There is emerging opportunity to dispatch soft open points to improve the operation performance of active distribution network. This paper proposes an optimal dispatch strategy to improve the network balancing performance, where a new type of phase-changing soft open point is installed. First, a new type of phase-changing soft open point with full-phase changing ability is introduced to balance the three-phase power flow. Then, the optimization model is formulated for phase-changing soft open points dispatching to minimize the total cost of distribution network. Furthermore, the model is formed as a constrained Markov decision process and efficiently solved by the augmented Lagrangian-based safe deep reinforcement learning algorithm featuring the soft actor-critic method. Finally, numerical simulations are conducted to validate the effectiveness, accuracy, and efficiency of the proposed method.

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.