With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

This paper presents a novel approach to multi-type distributed generation (DG) planning based on the analysis of investment and income brought by grid-connected DG. Firstly, the timing characteristics of loads and DG outputs, as well as the environmental benefits of DG are analyzed. Then, on the basis of the classification of daily load sequences, the typical daily load sequence and the typical daily output sequence of DG per unit capacity can be computed. The proposed planning model takes the location, capacity and types of DG into account as optimization variables. An improved adaptive genetic algorithm is proposed to solve the model. Case studies have been carried out on the IEEE 14-node distribution system to verify the feasibility and effectiveness of the proposed method and model.

As the realization form of integrated energy system, integrated energy park is the key research object in the field of energy. Actual integrated energy parks are often partitioned internally. In order to take into account the interests of each zone in the optimal scheduling of integrated energy parks, a double-layer optimal scheduling model of integrated energy parks based on non-cooperative game theory is proposed. First, according to the operation of the integrated energy park, the output and cost model of the park is established. Second, with the minimum daily cost as the upper layer objective and the highest energy efficiency of the cogeneration system as the lower layer objective, a double-layer optimal scheduling model is established. Then based on non-cooperative game, the optimal operation strategy of each zone is obtained through the game among all the zones. An integrated energy park is taken as an example, the results show that the proposed model can make zones adjust their operation strategies more reasonably, thus helping to reduce the cost of the park and improve energy efficiency.

With increased direct current (DC) load density and the penetration of a large number of distributed generation (DG) units in alternating current (AC) distribution networks (DNs); a planning approach that considers transforming some of the AC lines into DC lines and building the DC network is proposed. Considering the DG output uncertainty and the load fluctuation, a planning model for an AC/DC hybrid distribution network (DN) with the DG based on the construction of multi-scenario technology with timing characteristics is built. In the DG configuration planning model, the lines to be transformed into DC form the access location and the decision regarding the DC or AC form of the newly built lines are considered optimizing variables. The DG investment, the network and converters of the DG and load, the active power loss and the voltage stability are considered in the objective functions. An improved adaptive niche genetic algorithm based on the fuzzy degree of membership and variance weighting is used to solve the nested model. Finally, considering the improved electrical and electronic engineers 33 (IEEE33) node system as an example, the correctness and effectiveness of the proposed planning method are verified. Compared to the plan without transforming some of the AC lines into DC lines and building a DC network, more DG can be admitted, and the economic cost of the AC/DC hybrid DN is notably decreased when planning to transform some of the AC lines into DC lines and build a DC network. The active power network loss and the voltage stability index are similarly further optimized.

To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model based on quarter division and self-organizing map (SOM) neural network algorithm is proposed. In the model, the clustering feature vector is selected as the input vector of the SOM algorithm to perform DSR clustering analysis to get the different scenarios. In addition, to obtain the resource aggregation (RA) with good load characteristics, response characteristics and distributed generation (DG) consumption, a multi-scenario objective optimization aggregation model of DSR based on scenario partition is established, and an the model is solved by an improved niche evolutionary multi-objective immune algorithm. Finally, the case studies are given to verify the validity of the model.

In recent years, distributed generation and a large number of sensitive AC and DC loads have been connected to distribution networks, which introduce a series of challenges to distribution network operators (DNOs). In addition, the advantages of DC distribution networks, such as the energy conservation and emission reduction, mean that the voltage source converter based multi-terminal direct current (VSC-MTDC) for AC/DC distribution systems demonstrates a great potential, hence drawing growing research interest. In this paper, considering losses of the reactor, the filter and the converter, a mathematical model of VSC-HVDC for the load flow analysis is derived. An AC/DC distribution network architecture has been built, based on which the differences in modified equations of the VSC-MTDC-based network under different control modes are analyzed. In addition, corresponding interface functions under five control modes are provided, and a back/forward iterative algorithm which is applied to power flow calculation of the AC/DC distribution system including VSC-MTDC is proposed. Finally, by calculating the power flow of the modified IEEE14 AC/DC distribution network, the efficiency and validity of the model and algorithm are evaluated. With various distributed generations connected to the network at appropriate locations, power flow results show that network losses and utilization of transmission networks are effectively reduced.