With the increasing penetration of renewable energy generation, uncertainty and randomness pose great challenges for optimal dispatching in distribution networks. We propose a cloud-edge cooperative dispatching (CECD) method to exploit the new opportunities offered by Internet of Things (IoT) technology. To alleviate the huge pressure on the modeling and computing of large-scale distribution system, the method deploys edge nodes in small-scale transformer areas in which robust optimization subproblem models are introduced to address the photovoltaic (PV) uncertainty. Considering the limited communication and computing capabilities of the edge nodes, the cloud center in the distribution automation system (DAS) establishes a utility grid master problem model that enforces the consistency between the solution at each edge node with the utility grid based on the alternating direction method of multipliers (AD-MM). Furthermore, the voltage constraint derived from the linear power flow equations is adopted for enhancing the operation security of the distribution network. We perform a cloud-edge system simulation of the proposed CECD method and demonstrate a dispatching application. The case study is carried out on a modified 33-node system to verify the remarkable performance of the proposed model and method.

The increasing penetration of photovoltaic generation exacerbates the risk of voltage violations in distribution networks. One viable tactic for increasing the flexibility of regional distribution networks is to use the thermal inertia of community heating systems (CHSs). However, the slow heat dynamics in the heating network brings difficulties in synchronous joint operation decisions of the integrated system. In this paper, we propose a novel power-to-temperature sensitivity model (PTSM), equating the whole CHS to a flexible electric load with thermodynamic characteristics. It denotes the direct relationship between the average water temperature change and the electric power consumed by ground-source heat pumps (GSHPs). A robust collaborative voltage optimization model is then developed from the perspective of power distribution system operators. The PTSM provides accurate prediction of CHS temperature variations to construct the operation constraints of GSHPs. This mixed integer programming problem is solved by the column-and-constraint generation algorithm. The test results of an actual residential community demonstrate that the PTSM can ensure the prediction errors of the CHS average temperature within +/- 0.4 degrees C in case of the optimal time resolution and aggregated heating load location. An IEEE 69-bus distribution network with several CHSs is studied to show that, the flexibility of CHSs can achieve an extra 17.25% reduction of the daily voltage deviation. The proposed method has significant advantages in computation time and robustness. (C) 2021 Elsevier Ltd. All rights reserved.

For online dispatch, it is necessary to formulate the influence of ultra-short-term forecasting errors of renewable energy (RE) to guarantee the power grid safety. As a basic evaluation tool, the stochastic power flow (SPF) method should meet the efficiency requirements of online decision making. In this paper, the Cauchy distribution, which has high peak and fat tail characteristics, is used to fit the shape of RE ultra-short-time forecasting errors. With the formulation of the Cauchy distribution, an analytical SPF method considering the uncertainty of RE is proposed. This analytical SPF method ensures a fast calculation speed with high accuracy. Therefore, it is applicable for real-time applications. Numerical test results justify the superiority of the proposed Cauchy method in terms of accuracy compared to conventional methods.

With the increasing penetration of the photovoltaic (PV) in the distributed grid network, the dynamic response analysis of the system becomes more and more complex and costs lots of computational time in the simulation. To cut down the computational resources while guaranteeing the accuracy, this paper proposes a data-driven hybrid equivalent model for the dynamic response process of the multiple PV power stations. The data-driven hybrid equivalent model contains the simple equivalent model and data-driven error correction model. In the equivalent model, the distributed PV power stations in the same branch are equivalent to one power station model based on the parameter equivalence and feeder equivalence. The data-driven error correction model tracks and corrects the difference of dynamic response between the equivalent model and precise model. The ensemble Gated Recurrent Unit (GRU) model based on the bagging ensemble structure utilizes the simple equivalent dynamic response as input to learn the dynamic response errors. The simulation results validate the super-performance of the proposed model both in the response speed and accuracy.

Increasing energy efficiency and reducing pollution to the environment, a comprehensive energy system is of great significance for building an energy internet and promoting the transformation of the energy structure. Energy demand forecasting plays an important role in optimizing comprehensive energy system planning, and its accuracy is of great significance for maintaining stability and economic operation of the system. A load forecasting method based on Long Short-Term Memory Network Model (LSTM) is proposed in this paper. Firstly, calculating the mutual information load value of the past 1 hour time to the past 168 hour times, and the time to be predicted, and then using the maximum correlation and minimum redundancy to filter the input variables. Finally, on the basis of selecting the optimal input variable set, the multi-element load forecasting model of integrated energy system based on long-short-term memory neural network (LSTM) is established to realize the load data forecasting of the regional integrated energy system.

Multi-energy virtual power plants intend to aggregate multi-energy distributed resources and provide ancillary service to the power system. To exploit more flexibility of the synergy of multi-energy, multiple types of energy conversion equipment and thermal inertia of the commercial buildings are considered in this paper. A capacity limitation service model based on multi-parametric programming is proposed to overcome the difficulty that the power of the demand side distributed resources is difficult to be accurately controlled. Case studies based on an actual multi-energy industrial park in Beijing are conducted to validate the effectiveness of the proposed model.

This work proposes a distributed optimization strategy for integrated energy system (IES) based on alternating direction method of multipliers (ADMM). Firstly, uncertain factors from source and load sides are analyzed, and scenario method is utilized to describe their stochastic characteristics. Then, IES optimal scheduling model is established. Next, the principle of ADMM is clarified and aforementioned model is thus reformulated via ADMM to realize the distributed solution for multi-energy complementation. Finally, the effectiveness and feasibility of proposed strategy are verified by the results of case study.

The gas and steam combined cycle uses natural gas and realizes the supply of cold, heat and power based on the principle of energy cascade utilization. It has the advantages of high energy efficiency and less pollutant emission, so its application scale is extending. In this paper, the mathematical models of equipment in the energy station, which consists of gas and steam combined cycle, are established. Then the objective function and constraint conditions for multi-objective optimization considering economic and environmental benefits are proposed. The Nash negotiation method is used to find the solution to this multi-objective optimization problem on the Pareto frontier. Since the device models are properly linearized, each sub-problem on the Pareto front becomes a MILP problem, which can be solved by commercial solvers. Finally, a case study based on an actual energy station is carried out and the results of single-objective optimization and multi-objective optimization are compared to illustrate the Pareto effectiveness of the Nash Bargain solution.