The article divides the electromechanical transient simulation of large AC and DC power grids into AC network and DC network. With reference to the modeling method of AC network, all DC transmission systems are processed as a DC network for unified modeling and simulation. The modeling of the DC transmission system is extended from the components embedded in the AC system to the dimension of the DC network. The AC and DC networks exchange information at the commutation buses. Through the establishment of mathematical models of LCC commutation nodes, VSC commutation nodes, DC loads, DC lines, etc., the formation and solution methods of the DC network equations are explained in detail. The proposed method is simulated through the example system. The results show that, the proposed method can effectively adapt to many different types of HVDC power transmission forms.

To solve the problems such as mis-judgment of fault diagnosis of power grid caused by uncertainties, for instance, mis-operation or failure operation of protection and circuit breaker, including transmission error of alarm information, a multi-layer intelligent fault diagnosis method of power grid based on multi-source information fusion is proposed, which includes single-source information diagnosis layer containing switching value diagnosis and electrical quantity diagnosis, and multi-source information fusion diagnosis layer. Finally, a case study of a power grid topology shows that the proposed multi-layer fault diagnosis method can overcome the uncertainties of protection and circuit breaker maloperation, refusal to act and transmission error of alarm information, and accurately identify fault components. The effectiveness and fault tolerance of the proposed method are verified, and it has engineering practical value and good application prospect.

The fluctuation of distributed generation (DG) output will cause grid voltage volatility. When high-resolution DG forecast data is used, the solving process of the traditional distribution network reconfiguration model considering the suppression of voltage volatility will involve a lot of complicated power flow calculation. Aiming at the above problems, a multi-objective reconfiguration strategy of distribution network is proposed based on the high-resolution forecast data of DG output. The voltage volatility index is established according to node topological correlation and the fluctuation of the DG output. The characteristic values of DG output in the reconfiguration period are extracted according to the 1 min resolution forecast data; taking the index of voltage fluctuation and the minimum loss of active power network as objective function, the multi-objective reconfiguration model of distribution network is constructed considering the operation constraints of distribution network. The multi-objective particle swarm optimization algorithm based on Pareto optimal solution set is used to solve the reconfiguration model, and the optimal compromised solution is obtained from Pareto optimal solution set. The simulation results from the improved IEEE 33 node test system show that the proposed reconfiguration strategy can realize safe and economic operation of the distribution network.

The uncertainty of measurement makes the estimation accuracy of traditional methods difficult to meet the requirements of scheduling. The interval analysis is used to describe the measurement uncertainty. The dynamic interval optimization model of state variables of active distribution network is established, and the dynamic feasible region of state variables is obtained by using linear programming method. Based on the uncertainty of measurement and taking the highest accuracy of measurement point as the objective function, the robust state estimation model is established and the state estimation value is solved by modern interior point method. Taking the dynamic feasible region of the state variable as the constraint and the median value of the interval as the initial value, compared with the actual physical constraint and the maximum value of the historical data as the constraint, the range of solving the state variable is reduced and the amount of calculation is reduced. The feasibility and effectiveness of this method are verified by IEEE 30, IEEE 118 and actual distribution network systems. Combined with the evaluation function, a new evaluation index is proposed in the test of tolerance. Under three different constraints, the results of state estimation are compared, and the feasibility and accuracy of the proposed method are proved. At the same time, it can effectively track and estimate the voltage of the distributed generation access node.

When faults occur in microgrids (MGs), MG reconfiguration is required. To improve the efficiency of reconfiguration, a hierarchical optimization method for MG reconfiguration is proposed, where deep belief networks (DBNs), a typical model for deep learning, is introduced, which offers a way to avoid calculating power flow in the process of the MG reconfiguration. In this method, the first level optimization is obtaining the on/off status of breakers based on the supply-demand balance in MGs. In the second level optimization, the trained DBNs are used to obtain network losses and node voltages, etc., when the on/off status of breakers, are given to the DBNs, which is a good way to reduce time of reconfiguration. In addition, an evaluation method is also proposed, where the analytic hierarchy process (AHP) is employed to adjust the weights of preferences in a comprehensive evaluation function, so it offers the diversity of decisions for the MG reconfiguration. Finally, to test the proposed method, a modified IEEE 33-bus system is built in MATLAB for simulations. Compared to those methods where power flow is recalculated under different on/off status of breakers, our method can obtain the same reconfiguration scheme, but the time of reconfiguration is only one-seventieth of that of other methods. Furthermore, in terms of the proposed evaluation method, reconfiguration schemes can be selected under different preferences.

In view of the residual current in the passive arc suppression method for the single-phase grounding faults in the distribution network, and the active arc suppression method has the problems of large equipment capacity and high cost, this paper proposes a series connection method of line-phase converters and active compensation devices. The hybrid compensation method uses the line-phase converter to connect the electromotive force of the faulty phase power supply to the neutral point in reverse phase. Taking into account the voltage deviation on both sides of the line-phase converter, the active compensation device is used to compensate this deviation to achieve the purpose of accurate compensation. The compensation device adopts a proportional resonance control method to track and control the voltage on the secondary side of the line-phase converter in real time, so that the sum of the voltage on the secondary side of the line-phase converter and the output voltage of the active device is always equal to the opposite of the electromotive force of the fault phase-phase power supply. A single-phase grounding fault hybrid compensation simulation model of 10 kV distribution network is built using MATLAB/Simulink, the results show that the method can realize single-phase grounding fault arc suppression and greatly reduce the capacity of the active compensation device.

In order to efficiently use the resources on both sides of supply and demand in the integrated energy system and achieve optimal scheduling, the paper takes the integrated energy system of the park with four loads of electricity, gas, cooling and heat as the object, and constructs an optimal scheduling model based on the price guidance through comprehensively considering electricity and natural gas demand response to ensure the interests of both the business and the park. Firstly, a comprehensive energy system model of the park is established based on the energy bus; then, a demand response strategy for multiple types of loads is established based on the price elasticity of demand; finally, an optimal scheduling model considering the price guidance mechanism is proposed, and the model is solved by particle swarm algorithm combined with mathematics optimization software Gurobi. Simulation analysis shows that the method can guide the park users to buy the energy, and reduce the transmission power peak valley difference in the supply of the tie-line, and improve the overall economy of the system on the basis of the supply-demand double side needs and interests constraints.

With the deep integration of Power Internet of Things and Energy Internet, more and more terminal devices and users are connected to the power distribution network, how to allocate cloud-side resources to provide computing support for energy management applications has great research significance. This paper proposes an offloading cost based cloud-edge collaborative scheduling method for distribution network energy management applications. Firstly, this paper introduces the microservice model of energy management applications and the cloud-side collaborative application processing structure based on docker technology, as well as expounds the microservice offloading mechanism. Then, the mathematical model of microservice, edge node and container queue are established. Based on the two different offloading paths, which are edge-cloud offloading and edge-edge offloading, the costs of computing delay, queuing delay and communication delay are considered respectively, and the optimal offloading path are determined by solving the objective function to minimize the system offloading cost. Finally, the effectiveness of the proposed method are verified by simulation.

Tampering with the data of electricity meters is a typical electricity theft. For such electricity theft behaviors, the existing detection methods require a labeled data set or additional power system state information, which is difficult to obtain or has a large error with the actual value. Therefore, it is urgent to utilize the lower dimension data to realize the detection of electricity theft behavior. In this paper, a novel fusion detection method is proposed by combining the maximum information coefficient (MIC) technique and the clustering by fast search and find of density peaks. This method uses MIC to measure the correlation between management line loss and specific behaviors of consumers and uses CFSFDP to locate abnormal electricity consumers with high applicability, which can detect various types of electricity theft. This paper also uses the Irish smart meter data set to verify the algorithm, and the good performance of the proposed method is proved by the result.

The virtual power plant can effectively solve the problem of the lack of effective coordination and control among independent, multiple small and micro entities. As electricity reform has entered a more different phase in China, virtual power plants are gradually participating in market competition as comprehensive e-commerce vendors. Starting from the retail market, this paper proposes a two-stage double-layer random bidding strategy model in which virtual power plants participating in the spot joint market. In the first stage, based on the output characteristics of multiple small and micro entities such as distributed energy, power users, and distributed energy storage, a virtual power plant model that aggregates multiple small and micro entities is constructed. In the second stage, based on the Stackelberg leader-follower game, a dual-layer model of joint bidding strategy in the spot market is proposed, which takes virtual power plants as the bidding body and takes into account both income and risk. The upper level makes strategic quotations with the goal of maximizing the revenue of the virtual power plant; the lower level makes a joint day-ahead clearing of the multi-level market with the goal of minimizing power purchase costs. In addition, multi-scenarios are introduced to describe the risks brought by the uncertainty of competitors to the benefits of virtual power plants during the transaction decision-making game. The analysis of the example verifies the feasibility and effectiveness of the proposed model.

In the common pricing methods, real-time electricity price is too complex, idealistic and can not accurately reflect the law of real-time electricity price and cost, and wind and photovoltaic power output is uncertain. In view of this, under the guarantee of the interests of power sales companies, in order to improve the satisfaction and acceptability of users, taking power selling companies as the object, a hybrid pricing mechanism considering the uncertainty and correlation of wind and solar output is proposed. The Latin hypercube sampling method is used for hierarchical sampling and correlation control of wind and photovoltaic power output. With the goal of maximizing the profit of the power sales company, the optimization calculation is carried out, and the influence of the uncertainty and correlation of wind and photovoltaic power output is analyzed. The IEEE 33 node example shows that the hybrid pricing mechanism can take into account the interests of power sales companies and user satisfaction, and is a more reasonable pricing mechanism.

Market power monitoring is an important part of power market supervision and an indispensable means to realize the benign operation of the market. With the gradual liberalization of electricity market, the electricity retail market continues to expand, but the corresponding retail market monitoring mechanism is not sound. The paper analyzes the abuse of market power in the electricity retail market, designs a market power monitoring indicator system, clarifies the measurement standards of market power monitoring indicators, proposes a monitoring method of market power in the whole process of retail market transaction in three stages: before, during and after the event, and uses the grey comprehensive evaluation method to evaluate the market behavior of the electricity sales companies. Finally, by designing the examples of pre-prevention, in-process monitoring, and post-analysis, the effectiveness of the method in this paper is verified for the whole-process monitoring of the electricity retail market, which provides a reference for establishing the electricity retail market monitoring system in the future.