The principal motivation of the marine predators algorithm (MPA) is the common foraging technique, including Levy and Brownian motions in ocean predators coupled with optimal contact intensity policy in predator–prey biological interaction. This paper proposes an improved marine predators algorithm (IMPA), which is an extension of the original MPA. The suggested improvements lead to rapid convergence and avoid local minima stagnation for the original MPA. IMPA controls the active and reactive power injected into distribution systems to minimize the total system losses and the total voltage deviations and maximize the voltage stability and improve the distribution system's overall performance. On the one hand, the proposed IMPA determines the optimal location and active power (location and size, respectively) of distributed generation (DG). On the other hand, the IMPA controls reactive power by optimally placing and sizing the shunt capacitors (SCs) and determining the PF of DGs. Two standard test systems, 69-bus and 118-bus distribution networks, are considered to prove the proposed algorithm’s efficiency and scalability. Results of the proposed IMPA are compared with those obtained by MPA, AEO, and PSO algorithms. The findings of the simulation results demonstrate that the proposed IMPA can effectively find the optimal problem solutions and beats the other algorithms. Moreover, the framework of multi-objective IMPA outperforms based on MPA in terms of the performance measures of diversity, spacing, coverage, and hypervolume.

Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks

To solve the coordinated dispatch and time-space coupling problem in the distribution network with distributed generations (DGs), a novel two-stage (Heuristic search and Variable correction) dynamic reactive power dispatch strategy is proposed in this paper. Meanwhile, according to the analysis of Gauss-type and Z-type membership function, a new objective function is put forward to achieve the balance between network loss and voltage deviation. First, the pre-optimization is made in Niche genetic algorithm to coordinate on-load tap changer (OLTC), capacitor banks (CBs), and DGs. Second, the sequential fuzzy c-means (SFCM) based on artificial bee colony (ABC) algorithm is proposed and then the Heuristic search based on this method is used to formulate the day-ahead plans of OLTC and CBs. Finally, the dispatch plans of OLTC and CBs are applied to recalibrate the day-ahead dispatch plans of DGs. Comparing with existing schemes, strategies and optimization approaches based on diverse evaluation indicators, optimization results on IEEE 33-bus, IEEE 14-bus, and PG&E 69-bus test systems verify the practicality and efficiency of the proposed method. In addition, the effects on the optimization results with different adjustment step and action time of the discrete adjust devices are discussed by the IEEE 33-bus test system.

Two-Stage Dynamic Reactive Power Dispatch Strategy in Distribution Network Considering the Reactive Power Regulation of Distributed Generations

The growing penetration of 5G base stations (5G BSs) is posing a severe challenge to efficient and sustainable operation of power distribution systems (PDS) due to their huge energy demand and massive quantity. To tackle this issue, this paper proposes a synergetic planning framework for renewable energy generation (REG) and 5G BS allocation to support decarbonizing development of future PDS. The problem is formulated as a multi-objective interval optimization (MOIO) model, wherein the 5G BS siting and sizing are co-optimized with the REG capacity allocation and their operating strategies to minimize the total economic cost and carbon emissions of the PDS. The potential flexibility benefits achievable from 5G BS operation (as responsive load demands to PDS) are explicitly considered in the proposed planning formulation by accounting for the effects of both transmit power control and on-site energy storage battery dispatch. Moreover, we adopt an interval approach to capture the uncertainties involved in the system (including REG output, market prices, carbon emissions factor, and power/communication load demands). In order to solve the proposed MOIO problem efficiently, a constrained interval multi-objective optimization evolutionary algorithm based on decomposition (CIMOEA/D) is developed by combining constraint handling, interval-based Tchebycheff aggregation function and interval ensemble comparison, which could achieve Pareto optimal solutions and an interval-formed Pareto front to the problem with all the uncertain information fully preserved in the decision-making process. The proposed model is demonstrated on a modified IEEE 33-bus test case, and the simulation results verified the effectiveness of the suggested approach. 

Synergetic renewable generation allocation and 5G base station placement for decarbonizing development of power distribution system: A multi-objective interval evolutionary optimization approach

Fully decentralized multiarea reactive power optimization considering practical regulation constraints of devices

A multi-period scheduling strategy for ADN considering the reactive power adjustment ability of DES

Reactive Power Optimization Dispatch Strategy in Distribution Network with Distributed Generators

The future energy system will integrate multiple energy sources such as electricity, gas, and heat. The mode of independent operation and dispatch of each subsystem will not be suitable for the operation and dispatch of the tightly coupled integrated energy system. For this reason, the integrated demand response of electricity, gas, and heat is introduced into the integrated energy system, in which a 5G base station is taken into consideration for the electric load, to give full play to the scheduling potential of the load side. Then, in order to improve the economy of system operation and the level of new energy consumption, the day-ahead optimal energy flow scheduling model is constructed to minimize the total operating cost of the system. Based on the difference in steady-state transmission characteristics of electricity/gas/heat and the response time of energy equipment, the intraday optimization stage is decomposed into two-time scales. In the longer time scale, the gas and heat subsystems are dispatched and the integrated gas and heat load response is activated. While in the shorter time scale, the power subsystem is finely dispatched and the electric load demand response is activated. Finally, based on the theory of second-order cone relaxation and incremental piecewise linearization, the non-convex and nonlinear problems in the day-intraday optimization model are dealt with, and the day-intraday optimal scheduling strategy of the multi-energy flow integrated energy system is formulated. The results of calculation examples show that the mixed time-scale coordinated scheduling considering 5G base station participating in comprehensive demand response can ensure the safe and economical operation of the system, and realize resource complementarity and energy mutual benefit.

Coordinated Operation Strategy of Multi-energy Flow System with 5G Base Station Participating in Comprehensive Demand Response

The traditional single form of energy supply can no longer meet the needs of development. The integrated energy system has significant advantages in improving the energy utilization rate, but the participation of multiple subjects brings some difficulties to the operation optimization of the system. In this paper, a park-level integrated energy system composed of integrated energy operators and users is proposed and their benefit models are constructed, and the users' model is linearized. Since they influence each other when pursuing the maximum of their own interests, the master-slave game idea is adopted to analyze the interaction mechanism of them, and the particle swarm optimization algorithm and linear programming are used to solve the problem. Finally, the feasibility of the proposed method is verified by simulation.

Study on Master-Slave Game Strategy of Integrated Energy System Considering Users Demand Response and Energy Balance

Optimizing the transmission curve of DC power transmitted to Henan Province is of great significance for serving the new energy consumption of Henan Province and improving the overall operational efficiency of the power system. Based on the analysis of the net load characteristics of the power system in Henan Province, a two-step optimization method for the transmission curve of DC power transmitted to Henan Province is proposed. On the premise of meeting the full amount of transmission energy in the power transmission agreement, reasonably determine the transmission power, duration, and period distribution in the peak and valley sections of the power transmission curve. Through the simulation of production, the effect of optimizing the power transmission curve in matching the power demand of the provincial grid, reducing the abandonment of new energy, reducing the cost of ancillary services, and electricity purchases are evaluated quantitatively. This method has reference significance for provincial power companies to organize curve transactions and coordinate system peak shaving.

Optimization of UHVDC power transmission curve for auxiliary peak shaving

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Papers

Reactive Power Optimization Dispatch Strategy in Distribution Network with Distributed Generators

Coordinated Operation Strategy of Multi-energy Flow System with 5G Base Station Participating in Comprehensive Demand Response

Study on Master-Slave Game Strategy of Integrated Energy System Considering Users Demand Response and Energy Balance

Optimization of UHVDC power transmission curve for auxiliary peak shaving