Optimal scheduling of combined cooling, heating, and power microgrid based on a hybrid gray wolf optimizer

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints

A new optimal under-voltage load shedding scheme for voltage collapse prevention in a multi-microgrid system

The sparrow search algorithm (SSA) is a metaheuristic algorithm developed based on the foraging and anti-predatory behavior of sparrow populations. Compared with other metaheuristic algorithms, SSA also suffers from poor population diversity, has weak global comprehensive search ability, and easily falls into local optimality. To address the problems whereby the sparrow search algorithm tends to fall into local optimum and the population diversity decreases in the later stage of the search, an improved sparrow search algorithm (PGL-SSA) based on piecewise chaotic mapping, Gaussian difference variation, and linear differential decreasing inertia weight fusion is proposed. Firstly, we analyze the improvement of six chaotic mappings on the overall performance of the sparrow search algorithm, and we finally determine the initialization of the population by piecewise chaotic mapping to increase the initial population richness and improve the initial solution quality. Secondly, we introduce Gaussian difference variation in the process of individual iterative update and use Gaussian difference variation to perturb the individuals to generate a diversity of individuals so that the algorithm can converge quickly and avoid falling into localization. Finally, linear differential decreasing inertia weights are introduced globally to adjust the weights so that the algorithm can fully traverse the solution space with larger weights in the first iteration to avoid falling into local optimum, and we enhance the local search ability with smaller weights in the later iteration to improve the search accuracy of the optimal solution. The results show that the proposed algorithm has a faster convergence speed and higher search accuracy than the comparison algorithm, the global search capability is significantly enhanced, and it is easier to jump out of the local optimum. The improved algorithm is also applied to the Heating, Ventilation and Air Conditioning (HVAC) system control optimization direction, and the improved algorithm is used to optimize the parameters of the HVAC system Proportion Integral Differential (PID) controller. The results show that the PID controller optimized by the improved algorithm has higher control accuracy and system stability, which verifies the feasibility of the improved algorithm in practical engineering applications.

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Multi-Strategy Improved Sparrow Search Algorithm and Application

Power systems are susceptible to disturbances due to their nature These disturbances can cause overloads or even contingencies of greater impact In case of an extreme situation, load curtailment is considered the last resort for reducing the contingency impact, its activation being necessary to avoid the collapse of the system However, load shedding systems seldom work optimally and cause either excessive or insufficient reduction of the load To resolve this issue, the present paper proposes a methodology to enhance the load curtailment management in medium voltage distribution systems using Particle Swarm Optimization (PSO) This optimization seeks to minimize the amount of load to be cut off Restrictions on the optimization problem consist of the security operation margins of both loading and voltage of the system elements Heuristic optimization algorithms were chosen, since they are considered an online basis (allowing a short processing time) to solve the formulated load curtailment optimization problem Best performances regarding optimal value and processing time were obtained using a PSO algorithm, qualifying the technique as the most appropriate for this study To assess the methodology, the CIGRE MV distribution network benchmark was used, assuming dynamic load profiles during an entire week Results show that it is possible to determine the optimal unattended power of the system This way, improvements in the minimization of the expected energy not supplied (ENS) as well as the System Average Interruption Frequency Index (SAIDI) at specific hours of the day were made

https://www.mdpi.com/1996-1073/13/12/3236/pdf

Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks

This paper presents a model to forecast load in short-term (24 h) aiming to support decision-making during contingencies in power distribution systems. The methodology is based on the calculation of both, coefficients and angles of the Fourier series in order to characterize the load curve. These parameters are fitted using historical load reports. The proposed methodology was performed using the data of two power transformers to show the behaviour of the demand curve for weekdays as well as for weekends. The quadratic mean errors obtained using the proposed algorithm and according to the computational efficiency in the fitting of the coefficients, it is possible implement the algorithm in the power systems control and operation planning.

Short-Term Demand Forecast Using Fourier Series

To improve decision-making during contingencies in power systems and reduce the impact while a failure is repaired, it is necessary to know the consequences oflosing different elements of the system. These consequences need to consider the current system conditions and the short-term forecasting of the demand. Hence, this work proposes a procedure to run N-1 contingencies by using load forecasting obtained from historical records. This allows to know the assets in the system that present overloads or voltages outside of the security limits and can affect the security operation of system in a horizon of 24 h. To validate the procedure, the IEEE transmission system of 39 buses was used. To forecast the load, a script in Python using historical load profiles was implemented. To generate the list of assets with violations during contingencies, the pandapower module of Python was used in order to run the load flow. As a result, a list of affected assets was obtained per each hour of the day when lines and transformers were disconnected.

Laura M. Cruz

Papers

Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks

Short-Term Demand Forecast Using Fourier Series

On-Line Contingency Assessment Using Short-Term Load Forecasting