Unit commitment and multi-objective optimal dispatch model for wind-hydro-thermal power system with pumped storage

An improved hybrid particle swarm optimization algorithm based on adaptive inertia weight is proposed for job shop scheduling problem(JSSP). In order to overcome the shortcomings of the standard particle swarm optimization algorithm(PSO) in the optimization process, such as slow iteration speed, low precision and easy to fall into local optimal, simulated annealing algorithm(SA) and PSO are combined(SAPSO); In order to balance the global search ability and local search ability of particles, the adaptive weight of each particle is dynamically adjusted in this paper. The improved hybrid algorithm utilizes the probabilistic mutation capability of SA, which can accept both the good solution and the bad solution with a certain probability when accepting the new solution, and can get rid of the local optimal solution of the algorithm, which not only improves the flexibility and diversity of the algorithm, but also improves the diversity of particles. Adding adaptive weight to improve inertia weight in PSO makes it possible to dynamically adjust parameter factors according to fitness. Finally, the feasibility and efficiency of the improved algorithm are verified by the standard examples of JSSP.

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination; the roulette operator is used to select excellent particles; then, the selected particles are crossed according to their adaptive crossover probabilities; when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means’ sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.

In this article, an improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to solve the optimization problem of controller parameters in the design of a permanent magnet synchronous motor (PMSM) servo system. The proposed algorithm presents the directional mutation operation to the particles, which fixes the position of some particular particles so as to enhance the searching ability to some remote regions. In order to cooperate with directional mutation operation, the updating formula of particles velocity is ulteriorly improved. Then, the proposed IHPSO algorithm is adopted to optimize parameters of the designed controller. A simulation and an experimental platform of the PMSM servo system are designed using a biological intelligence controller based on hormone regulation for the speed control and feedforward compensation for the position controller, where IHPSO is applied to the parameter optimization for speed and position controllers, which validate the effectiveness of the IHPSO algorithm and the designed control system.

Large scale grid-connected wind power will have a great impact on the power grid. In order to ensure the safetyof power grid operation, it is necessary to quickly adjust the load of thermal power plants to stabilize the fluctuation ofwind power output. The economy of thermal power unit is poor in low load operation, and the safety and service life of theunit will be reduced when the load is adjusted frequently. In order to solve the problems, wind and thermal powercomplementarity is proposed, and the wind farm and thermal power plant constitute a virtual power plant (VPP) toparticipate in the grid dispatching. A multi-objective optimal dispatching model of VPP considering economy and rapidity isestablished, which is solved by improving genetic algorithm. The results show that the multi-objective optimal dispatchingmodel can make the economy of VPP the best on the premise of meeting the requirements of grid dispatching time throughthe simulation of differentscenarios. Furthermore, the strategy of selecting the weight coefficient of multi-objectiveoptimization model is given, and the wind power consumption capacity of VPP is analysed.

Firstly, aiming at the tactical relationship between multiple interceptors and targets, this paper proposes an indicator function and establishes a target allocation model for multiple interceptors. Then it designs an improved hybrid particle swarm optimization algorithm. This algorithm uses the linear decreasing weight method to solve the contradiction between global search ability and convergence precision. Hybrid operators in genetic algorithms are introduced to avoid local optimal solutions. And combined with the idea of tabu search, the algorithm reduce the round-trip search of the original algorithm. The simulation results show that the designed algorithm can give a better solution to the target allocation problem of multiple interceptors.

To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, the proposed method introduces a Tent chaotic mapping initialization mechanism, a Logistic-based dynamic inertia weight adjustment strategy, and adaptive Gaussian perturbation optimization to achieve precise control of the agricultural machinery’s driving orientation angle. A comprehensive path planning model is constructed with the objectives of minimizing the effective operation path length, reducing turning frequency, and maximizing coverage rate. Furthermore, cubic Bézier curves are employed for path smoothing, effectively controlling path curvature and ensuring the safety and stability of agricultural operations. The simulation experiment results demonstrate that the TLG-PSO algorithm achieved exceptional full-coverage operation performance across four categories of typical test fields. Compared to conventional fixed-direction path planning strategies, the algorithm reduced average total path length by 6228 m, improved coverage rate by 1.31%, achieved average labor savings of 96.32%, and decreased energy consumption by 6.45%. In large-scale comprehensive testing encompassing 1–27 field plots, the proposed algorithm reduced average total path length by 8472 m (a 5.45% decrease) and achieved average energy savings of 44.21 kW (a 5.48% reduction rate). Comparative experiments with mainstream intelligent optimization algorithms, including GA, ACO, PSO, BreedPSO, and SecPSO, revealed that TLG-PSO reduced path length by 0.16%–0.74% and decreased energy consumption by 0.53%–2.47%. It is worth noting that for large-scale field operations spanning hundreds of acres, even an approximately 1% path reduction translates to substantial fuel and operational time savings, which holds significant practical implications for large-scale agricultural production. Furthermore, TLG-PSO demonstrated exceptional performance in terms of algorithm convergence speed and computational efficiency. The improved TLG-PSO algorithm provides a feasible and efficient solution for autonomous operation of large-scale agricultural machinery.

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

The growing share of renewable energies needs more flexible services to balance their intermittency and variance. The existing coal fired units and electrical energy storage (EES) systems may play an important role in delivering flexible services. The value of their flexibility services, along with the value of renewable energies, has to be analyzed from the perspective of the power system, in which the capacity costs and operation costs of renewable energy power units, EES systems, and thermal power generation units have to be taken into consideration. An optimal model is built to analyze the renewable energy integration and the flexibility services delivered by the EES systems and thermal power units in a power system. Taking the existing thermal power units and EES systems in North China Power Grid as an instance, the overall cost of the grid is examined for the penetration of renewable energies and flexible service provision. The results show that the growing shares of renewable energies are affected by their capacity credits and flexibility sources in the grid, and that the potential of thermal power units to provide flexible services will be reduced due to the replacement of renewable energies for thermal power generation. The results also indicate that the thermal units may be dispatched to have priority to delivering flexible services for the renewable energy integration, and that the curtailment of renewable energies may be regarded as one type of flexible service. According to these results, policy and strategy recommendations are put forward to weigh the role of existing coal-fired units and EES systems in providing flexible services, and to improve their compensation mechanism and their coordination.

High fidelity modeling of pumped storage units for optimal operation of a multi-energy co-generation system

This work describes the benefits arising from the introduction of a hybrid wind - pumped storage unit into a non interconnected power system such as Rhodes' Island power system. Optimal power flow (OPF) is one of the main tools for optimal operation and planning of modern power systems. The OPF was applied to the Rhodes' power system which consists of thermal power plants and Renewable Energy Sources (RES, mainly wind turbines). In order to increase the RES penetration, pumped storage is applied. The pumped storage unit is considered to be connected to grid bus where a wind park exists. Actual data regarding the wind speed, load, thermal stations, lines, transformers and RES were imported in Matlab environment for the simulation of the power system. Various scenarios about the operation of the hybrid unit were examined. The scope was to achieve peak shaving during the high load demand period. Finally, the benefits of the investment were examined and evaluated. (9 pages)

The Hopfield network has been applied to the power system economic dispatch problem with very promising results. However, it has been found that the unit commitment problem cannot be tackled accurately within the framework of the conventional Hopfield network. This is due to the fact that both discrete and continuous terms must be considered to fully model the problem. This paper presents an augmented network architecture with a new form of interconnection between neurons giving a more general energy function containing both discrete and continuous terms. A comprehensive cost function for the unit commitment problem is developed and mapped to this energy function. Results show that this technique outperforms previous neural network methods. The new method also compares favourably with Lagrangian relaxation. Detailed results for a power system with thermal, hydro and pumped storage units are presented.

It is a technical difficulty for the safe and economic operation of active distribution network to control and manage distributed generators, distributed energy storage and load flexibly and effectively to eliminate the influence of distributed energy uncertainty on grid. In order to realize the full consumption of a large number of intermittent energy sources in active distribution network, this paper proposes an optimal control strategy for active distribution network considering energy storage system and controllable load. And a multi-objective optimal dispatching model with the lowest active power dispatching cost and the smallest intermittent energy reduction for active distribution network is established. The improved particle swarm optimization algorithm is used to solve the model. Finally, a simulation system is constructed to verify the effectiveness and feasibility of the proposed control strategy.

A flexible load active management method in optimization operation of distribution networks is proposed to discuss the significant role of active management of flexible load in regulating load curve. The models of translatable load, shiftable load and curtailable load are established and they are divided into price incentive flexible load and contract incentive flexible load firstly. Secondly, a multi-objective optimal dispatch model considering electricity purchasing cost, load peak valley difference and users' acceptance is established for different types of flexible load. Then, considering the proportion of different types of flexible load in different user types, the flexible load response model of residential load, industrial load and commercial load are obtained. Finally, the regulation effects of load curve and dispatch strategies of the overall flexible load are obtained by summarizing the dispatch strategies of different user types.

This paper proposes a hybrid eagle strategy with crow search algorithm (ES-CSA) as local optimizer to solve the unit commitment problem (UCP) in power systems. The algorithm aims to minimize total operational costs while considering pumped storage units as spinning reserves. The proposed methodology combines the exploration capability of ES with CSA's local search efficiency to determine optimal generator scheduling and power dispatch. The approach is validated using standard test cases from the literature, demonstrating improved convergence and cost reduction compared to existing methods. Results confirm the effectiveness of integrating pumped storage units in reducing overall system costs while maintaining reliable operation.

A combined multi-objective optimization model for degradation trend prediction of pumped storage unit