Spinning Reserve Constraints Research Articles

A novel algorithm for optimizing genset operations to minimize fuel consumption in remote diesel-RES microgrids

This paper addresses the challenge of reducing fuel consumption in Diesel-RES (Renewable Energy Sources) isolated microgrids, particularly focusing on Diesel Genset’s (DG) operation. The study introduces a basic rule based energy management system that serves as a platform to test out various DG operational strategies with a novel approach. Two optimization strategies—load dispatch optimization and unit commitment optimization—are explored to unequally distribute loads among different grid-connected DGs and sequence their start/stop based on predictive demand profiles respectively. Additionally, the integration of a spinning reserve-providing battery is investigated to alleviate DGs from their spinning reserve constraint, resulting in higher operational loads and consequently higher efficiency. The proposed model is applied on a case study of the Tahitian power system, demonstrating reductions in fuel consumption. The combined application of the proposed DG load dispatch and unit commitment optimizations, along with the integration a spinning-reserve-providing battery, yielded a 2.6 % reduction in fuel consumption and 6kt decrease in CO2 emissions over a year compared to a basic DG operation without a battery.

Hybrid team game algorithm for dynamic economic-emission electric power generation scheduling

The rising greenhouse gases, insufficient electricity production, and growing demand call for optimal thermal power generation scheduling to minimise fuel costs and emissions. Traditional optimisation methods are unable to find solutions due to non-convex objective functions and constrained convergence. Meta-heuristic techniques gain attention to address static and dynamic economic-emission generation scheduling challenges, owing to their adaptable nature and derivative-free structures. This paper proposes a Hybrid Team Game Algorithm (HTGA) that incorporates learning and exchange operators, simplex search, passing, mistake, substitution, and player within the field operators to solve the dynamic economic-emission generation scheduling (DEEPGS) of thermal units while considering multiple fuel options, valve loading effect, prohibited zone avoidance, meeting ramp-rate limits, and spinning reserve constraints. By evaluating generation scheduling at initial intervals, the forward strategy provides a solution to the dynamic EEPGS problem. A non-interactive approach uses the price penalty method to combine the objective functions, and heuristics are employed to explore feasible solutions using a replacement technique and proportional power sharing for unmet load demand. The algorithm's versatility is demonstrated across benchmark problems and electric power systems, as affirmed by Friedman and Wilcoxon's tests. Notably, HTGA requires fewer sensitive parameters, enhancing its practical applicability in addressing the DEEPGS of thermal units.

Reliability based computational model for stochastic unit commitment of a bulk power system integrated with volatile wind power

Reliable and economic operation of transmission systems is one of the most onerous problems faced by the grid operators as a penetration rate of wind energy is on rise. It is a crucial carbon-free source and alleviates dependency upon the conventional resources, though its stochastic and unpredictable nature can cause the risk of random component failures. Therefore, reliability evaluation of the system under wind power is of paramount importance and is suggested as the proposed study. A Reliability based Stochastic Unit Commitment (RSUC) model is proposed for a bulk power system integrated with volatile wind power. It is formulated using Mixed Integer Linear problem considering networks and spinning reserve constraints. The volatility of wind speed is modeled by generating different scenarios using K-means clustering approach with computation of correlation among them using autocorrelation factor and their transition probabilities is proposed using Markov Chain (MC). Further, the system's reliability is evaluated using Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). The proposed approach is investigated on the IEEE 24 Reliability Test System (RTS) under the various overloading and random component outage conditions. Further, its results effectiveness is compared and validated with the other existing approaches.

Adaptive H∞ event-triggered load frequency control in islanded microgirds with limited spinning reserve constraints

Using an islanded microgrid (MG) with large-scale integration of renewable energy is the most popular way of solving the reliable power supply problem for remote areas and critical electrical users. However, compared with traditional power systems, the limited spinning reserves and network communication bandwidth may cause weak frequency stability in the presence of stochastic renewable active outputs and load demand fluctuations. In this paper, an adaptive event-triggered control (ETC) strategy for a load frequency control (LFC) system in an islanded MG is proposed. First, a bounded adaptive event-triggered communication scheme is designed. This not only saves on network resources, but also ensures that the control center has a sensitive monitoring ability for the MG operating status when the frequency deviations have been effectively damped. Secondly, by fully considering the spinning reserve constraints and uncertain communication delays, the LFC system is described as a nonlinear model with saturation terms. Design criteria for ETC parameters are strictly deduced based on Lyapunov stability theory. Finally, an ETC parameter optimization algorithm based on random direction search is developed to reconcile the bandwidth occupancy and control performance. The effectiveness of the proposed method is verified in an MG test system.

Optimization scheduling of a wind–photovoltaic–gas–electric vehicles Community-Integrated Energy System considering uncertainty and carbon emissions reduction

Coupling renewable energy power generation, electric vehicles, combined cooling heating and power system, and energy storage system is a new way for Community-Integrated Energy Systems (CIES) to shift to a low-carbon, highly efficient, environmentally friendly society, which is the key task in coordinating flexible demand response with multiple uncertainties in renewable energy. To this end, an integrated demand response program is used to explore the potential interaction ability of electricity-gas-heat-cool-Electric Vehicles flexible loads in a scheduling model based on chance-constrained programming, based on which an integrated demand respond-enabled CIES with uncertain renewable generation was built, meanwhile, a ladder-type carbon trading mechanism was designed, and then the original model was transformed into a solvable mixed-integer linear programming model using Sequence Operation Theory and linearization methods. Furthermore, the objective function was constructed by using the deviation preference optimization strategy, daily comprehensive cost and daily carbon emissions were introduced to reflect the decision-makers’ different optimization preferences by using weight coefficients as double optimization objectives. Finally, the simulation shows that CIES operation can effectively balance economy and reliability by setting an appropriate confidence level for the spinning reserve constraint, meanwhile, the comparative result shows that EV charging in the CIES has a very significant environmental benefit in carbon dioxide emissions reduction, while the number of EVs exceeds 500, EV charging in the CIES starts to show a better economic benefit.

Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques.

Optimal Scheduling of Integrated Demand Response-Enabled Community-Integrated Energy Systems in Uncertain Environments

The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR)-enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and micro-gas turbine (MT), as links of multi-energy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory (SOT) and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. Simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a trade-off between economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the Hybrid Intelligent Algorithm in terms of both optimization results and calculation efficiency.

Solution to dynamic economic dispatch with prohibited operating zones via MILP.

Dynamic economic dispatch (DED) problem considering prohibited operating zones (POZ), ramp rate constraints, transmission losses and spinning reserve constraints is a complicated non-linear problem which is difficult to solve efficiently. In this paper, a mixed integer linear programming (MILP) method is proposed to solve such a DED problem. Firstly, a novel MILP formulation for DED problem without considering the transmission losses, denoted by MILP-1, is presented by using perspective cut reformulation technique. When the transmission losses are considered, the quadratic terms in the transmission losses are replaced by their first order Taylor expansions, and then an MILP formulation for DED considering the transmission losses, denoted by MILP-2, is obtained. Based on MILP-1 and MILP-2, an MILP-iteration algorithm is proposed to solve the complicated DED problem. The effectiveness of the MILP formulation and MILP iteration algorithm are assessed by several cases and the simulation results show that both of them can solve to competitive solutions in a short time.

Day-ahead Optimal Dispatch Model for Coupled System Considering Ladder-type Ramping Rate and Flexible Spinning Reserve of Thermal Power Units

In northern China, thermal power units (TPUs) are important in improving the penetration level of renewable energy. In such areas, the potentials of coordinated dispatch of renewable energy sources (RESs) and TPUs can be better realized, if RESs and TPUs connected to the power grid at the same point of common coupling (PCC) are dispatched as a coupled system. Firstly, the definition of the coupled system is introduced, followed by an analysis on its characteristics. Secondly, based on the operation characteristics of deep peak regulation (DPR) of TPUs in the coupled system, the constraint of the ladder-type ramping rate applicable for day-ahead dispatch is proposed, and the corresponding flexible spinning reserve constraint is further established. Then, considering these constraints and peak regulation ancillary services, a day-ahead optimal dispatch model of the coupled system is established. Finally, the operational characteristics and advantages of the coupled system are analyzed in several case studies based on a real-world power grid in Liaoning province, China. The numerical results show that the coupled system can further improve the economic benefits of RESs and TPUs under the existing policies.

Data-Driven Nonparametric Joint Chance Constraints for Economic Dispatch with Renewable Generation

This article presents data-driven nonparametric joint chance constraints (JCCs) for ramp-constrained economic dispatch. Power generated by renewable sources, such as solar farms, is modeled as an uncertain parameter that may not belong to any parametric class of probability functions. Thus, transmission line flow limitation, which time-independent constraints, are formulated as nonparametric JCCs joint over unknown cumulative distribution functions of renewable generation sources. In addition, thermal units' time-dependent spinning reserve constraints are modeled as nonparametric JCCs joint over scheduling time periods. An approach is presented based on a multivariate kernel density estimator to convert economic dispatch with data-driven nonparametric JCCs into an optimization model solvable by standard solvers. A reduced, more conservative risk level is calculated to ensure JCCs' satisfaction, given the distance between true unknown and estimated cumulative distribution functions by kernel density estimator. An initiation strategy is applied to speed up the optimization solution time. Numerical results show that using data-driven nonparametric JCCs enhances system reliability significantly as compared to using individual chance constraints and parametric JCCs.

Economic dispatch of multi-area integrated electricity and natural gas systems considering emission and hourly spinning reserve constraints

With the increasing growth of gas-fired units and power-to-gas facilities, the power system and natural gas system become an integrated electricity and natural gas system (IENGS), which brings new opportunities to mitigate the associated environmental issues and deal with various uncertainties from electricity load, renewable energy generation, and gas load. Different geographical locations may have many IENGSs which are connected by electric or gas transmission lines, thus formulating multi-area IENGSs. These multi-area IENGSs are not simple addition of individual IENGSs, since renewable energy may distribute unequally between different IENGSs, and excess renewable energy needs to be transmitted from one area to another, but is limited by potential tie-line congestions. To solve these problems, this paper proposes an economic dispatch model for multi-area IENGSs considering tie-line congestion, maximum allowable emission bounds and hourly spinning reserve constraints. The considered multi-area IENGS consists of multiple single-area IENGSs, among which wind energy distribution varies considerably. The maximum allowable emission bounds of carbon dioxide and nitrogen oxygen are separately set based on their different influences on the environment. The hourly spinning reserves are allocated for both the power system and the natural gas system, which are provided by fossil fuel-fired units, power-to-gas facilities, gas pipelines, and natural gas storage facilities. After that, the presented model is converted into a mixed-integer linear programming problem for higher computing efficiency. Case studies for integrated systems are analyzed to demonstrate the effectiveness of the proposed model.

A Short-Term Optimal Dispatch Model Considering Uncertain Wind Power Output

Aiming at the security and economic problems caused by wind power connected to the grid, a short-term optimal dispatch model for power system considering the uncertain wind power output is proposed. Although wind power is an important clean energy source, wind power output is characterized by volatility and randomness, which damages the system power balance and brings difficulties to dispatching. In this paper, wind power output characteristics are described by uncertain sets of wind power output. Considering the positive and negative spinning reserve constraints, a bi-level optimization model is constructed. According to the dual principle, the bi-level optimization model can be transformed into a solvable robust optimization scheduling model. Finally, the numerical simulation shows that the proposed method can realize the utilization of wind power, rationally arrange the commitment operation and output of thermal power units, which has good robustness to wind power output fluctuations to ensure the safe and economic operation of the system.

A mixed binary‐continuous particle swarm optimisation algorithm for unit commitment in microgrids considering uncertainties and emissions

Objective Using a mixed binary continuous particle swarm optimisation (PSO) with V-shaped quadratic transfer for solving UC in demand response integrated MGs, while the uncertainties are considered. Method A mixed binary continuous particle swarm optimisation (PSO) is used for optimisation. Results The UC results have been achieved for 6 different scenarios. The potential of load curtailment as an incentive-based demand response program has been approved in decreasing operation cost of MGs and the effect of emissions, spinning reserve constraint and bid functions on operation cost of MG has been investigated. Discussion The achieved results in different scenarios approve the outperformance of the proposed PSO over some benchmark optimisation algorithms.

Moth flame optimizer-based solution approach for unit commitment and generation scheduling problem of electric power system

Abstract This paper proposes a new approach based on the moth flame optimizer algorithm. Moth flame optimizer simulates the natural fervent navigation technique adopted by moths looking for a source of light. The proposed method is further improved by priority list-based ordering; the unit commitment problem (UCP) is a non-linear, non-convex, and combinatorial complex optimization problem. It contains both continuous and discrete variables. This further increases its complexity. Moth flame optimizer is very good at obtaining a commitment pattern: allocation of power on the committed units obtained by mixed-integer quadratic programming method. Heuristic search strategies are used to adopt for the repair of minimum up and downtime, and spinning reserve constraints. MFO effectiveness is tested on the standard UCP reference IEEE model buses 14 and 30, and 10 and 20 units. The efficiency of the projected algorithms is compared to classical PSO, PSOLR, HPSO, PSOSQP, hybrid MPSO, IBPSO, LCA-PSO, NPSO, PSO-GWO, and various other evolutionary algorithms. The comparison result shows that MFO can lead to all methods reported earlier in literature.

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints.

Unit Commitment Solution by Branch and Bound Algorithm

For a short period, to obtain a secured economic dispatch of power generation, Security Constrained Unit Commitment (SCUC) is programmed to commit the units by minimizing the operating costs while maintaining the balance between supply and demand in a deregulating environment. This paper discusses a vivid planning horizon of three and six periods distributed in a day taking into account the fixed cost, variable cost, start-up and shut-down cost, ramping up and down limits, minimum and maximum power bounds, spinning reserve constraints of the units and line load abilities which are all modelled to commit the generators in binary digits only. The proposed approach applies the Branch and Bound algorithm to test the system. The iterative process will converge, if the optimal solution exists. In this paper three and six bus systems are analyzed to prove the effectiveness of this model which can be extended for larger systems too.

Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

As the power systems in some large developing and developed countries are getting bigger, solving large-scale unit commitment (UC) is an urgent need and significant task to ensure their economic operation and contribute green energy consummation to society. In this article optimization models covering economy and environmental protection are established, and an improved binary artificial fish swarm algorithm (IBAFSA) is presented to solve the large-scale UC problems. The parameters of IBAFSA are improved by Levy flight and adaptive average visual distance to search space more actively, and a double threshold selection strategy is used to enhance the effectiveness of population evolution in the optimization. Meanwhile, a heuristic greedy search algorithm among the best individuals of all generations in the iterative process of the optimization is proposed, which is beneficial to improve computation convergence and reach the optimum solution. A fast constraints processing mechanism based on the heuristic modifying strategy of unit violation is established to handle the coupling between system spinning reserve constraint and unit minimum up and down time constraint. The effectiveness of the proposed approach is verified by the UC simulations of test systems of 10-1000 units, the IEEE 118-bus system, and a large-scale power system of 270 units. The numerical simulating results show that the proposed UC solution method can achieve the near-optimal solutions in a reasonable time, improve the economic and environmental benefits of a large-scale power system, and is a general method to adapt to the changes of the objective function and constraints of a UC optimization.

Risk‐averse energy management system for isolated microgrids considering generation and demand uncertainties based on information gap decision theory

There are many technical challenges for integration of renewable energy sources (RESs) in the context of microgrids. Among these challenges, spinning reserve energy management should be accurately considered in the microgrid scheduling system for a better system operation. This study presents a methodology to model and analyse a novel scheme to integrate RESs, particularly photovoltaic (PV) systems, in diesel generation-based isolated microgrids. The proposed approach considers the uncertainties of PV power generation and demand, simultaneously, by solving a bi-level multi-objective optimisation problem using information gap decision theory (IGDT). The proposed energy management system is formulated considering spinning reserve constraints and the uncertainties associated with PV power generation and load, by solving a unit commitment problem. This method, a non-probabilistic approach, does not require the probability density function of uncertain parameters and provides a robust framework to better understand the potential savings due to the PV integration. In order to test and perform the analysis, realistic data from a 20 MW hybrid PV project is used as a case study. Furthermore, the proposed method is compared with probabilistic techniques, such as Monte Carlo simulations and scenario-based stochastic programming technique. The presented studies demonstrate applicability of the proposed model for real microgrids.

Binary whale optimization algorithm and its application to unit commitment problem

Whale optimization algorithm is a novel metaheuristic algorithm that imitates the social behavior of humpback whales. In this algorithm, the bubble-net hunting strategy of humpback whales is exploited. However, this algorithm, in its present form, is appropriate for continuous problems. To make it applicable to discrete problems, a binary version of this algorithm is being proposed in this paper. In the proposed approach, the solutions are binarized and sigmoidal transfer function is utilized to update the position of whales. The performance of the proposed algorithm is evaluated on 29 benchmark functions. Furthermore, unpaired t test is carried out to illustrate its statistical significance. The experimental results depict that the proposed algorithm outperforms others in respect of benchmark test functions. The proposed approach is applied on electrical engineering problem, a real-life application, named as “unit commitment”. The proposed approach uses the priority list to handle spinning reserve constraints and search mechanism to handle minimum up/down time constraints. It is tested on standard IEEE systems consisting of 4, 10, 20, 40, 80, and 100 units and on IEEE 118-bus system and Taiwan 38-bus system as well. Experimental results reveal that the proposed approach is superior to other algorithms in terms of lower production cost.

New Strategy Based on Combined Use of Genetic Algorithm and Gradient to Solve the UC Problem: Theoretical Investigation and Comparative Study

This paper presents a comparative study between a new strategy based on hybrid Gradient-Genetic Algorithm method and metaheuristic methods for solving Unit Commitment problem. Strategies have been applied on the IEEE electrical network 14 bus test system for a variable load profile during a discretized margin of time (24-hour time requirement). The right choice of the initial population and the best knowledge of the technical constraints specific to each generator (power balance constraints, Spinning reserve constraints, minimum up time, minimum down time ) suggests the possibility of obtaining improvements in the time execution. The adopted strategy has presented high performance both for minimizing the production cost and for the rapidity of convergence to optimal solutions and is promising compared to Genetic algorithm.