Economic dispatch (ED) problem with valve-point effects of generation units is a non-smooth and non-convex problem. This paper presents a differential evolution algorithm based on multi-population (MPDE) to solve ED problems with valve-point effects. The proposed MPDE algorithm employs the evolutional methodology of multiple populations to overcome the limitations of the traditional differential evolution algorithm. In multiple populations, each population has its mutation strategy and parameters to enhance the searching ability. Moreover, information exchange among multiple populations to increase the diversity of individuals in a single population. In addition, the algorithm also introduces the normal distribution function to dynamically adjust the scaling factor and crossover rate to accelerate convergence speed. The proposed algorithm is tested on the 13-, 40-, 80-, and 140-unit test systems. The simulation results show that the MPDE algorithm can achieve much smaller variances compared with other intelligent algorithms. The proposed algorithm has significant performance in accuracy for solving economic dispatch problems with valve-point effects.

The continuous growth of the number of electric vehicles (EVs) increases the proportion of charging station load in the power grid, which has accelerated the distribution network upgrade including the topology structure change and the line renovation. As a result, the load prediction accuracy without considering the distribution network upgrade will be reduced. For the EV charging station load prediction considering the distribution network upgrade, this article proposes an EGAT-LSTM prediction method integrating edge aggregation graph attention network (EGAT) model and long short-term memory network (LSTM) model. The EGAT model can extract the key system information to a new node characteristic set integrating adjacency relationship, line impedance (R/X) and node data. The EGAT-LSTM prediction method through training learnable parameters captures the spatio-temporal correlations to reduce the non-regression learning of the prediction model. This method is tested on IEEE 33 and IEEE 69 bus distribution network systems. Simulation results show that for the EV charging station load prediction considering the distribution network upgrade, the proposed method improves training efficiency and prediction accuracy compared with the other methods without considering line characteristic.

With the rapid development of the new concept of energy internet, electric power systems often need to be investigated together with thermal energy systems. Additionally, to reduce pollution from gas emissions, it is very important to study the economic and emission dispatch of integrated electrical and heating systems. Hence, this paper proposes a multi-objective optimization dispatch model for a microgrid (MG) with a combined heat and power (CHP) system. This CHP-based MG system consists of a CHP unit, a wind turbine, a PV system, a fuel cell, an electric boiler, an electric storage, and a heat storage. It can exchange electricity with the distribution network and exchange heat with the district heating network. Minimum economic cost and minimum environmental cost are considered as the two objectives for the operation of this CHP-based MG system. To solve the two objective optimization problem, the multi-objective bacterial colony chemotaxis algorithm is utilized to obtain the Pareto optimal solution set, and the optimal solution is chosen by the Technique for Order of Preference by Similarity to Ideal Solution method. Finally, numerical case studies demonstrate the effectiveness of proposed model and method for the optimal economic and emission dispatch of the CHP-based MG system.