General Algebraic Modeling System Software Research Articles

Smart home energy management and active power loss analysis of a residential community

As the demand for energy continues its upward trajectory, judiciously managing the global energy resources has become increasingly imperative. One effective strategy to accomplish this efficiency is by strategically scheduling intelligent household appliances, a process that considers dynamic pricing schemes and the availability of renewable energy sources. This paper presents a comprehensive study that delves into optimizing smart home appliance utilization, considering electric vehicles and a modest rooftop photovoltaic installation. The study seeks to ascertain their potential impact on both energy consumption and cost mitigation. The proposed scheduling approach is centered on the optimal scheduling of smart appliances, harnessing the power of real-time pricing schemes to curtail the overall energy expenditure. The scheduling conundrum is meticulously modelled using General Algebraic Modeling System software and subsequently tackled by the CPLEX solver, leveraging mixed-integer linear programming techniques. Results reveal a remarkable achievement, with the potential for up to an 80.6 % reduction in the overall cost compared to the base case under various scenarios considering different approaches. To validate the integrity of the study, outcomes are juxtaposed with those of the base paper, and the superiority of the proposed method is evident from the results. Furthermore, the paper considers the ramifications of optimized scheduling on the voltage profile and overall system losses nearly 7 % less compared to the base case within the context of the IEEE 33-bus system. Thus, the presented work successfully analyzes the effect of household energy scheduling on the community level and scales up to a microgrid level.

A highly efficient, low-carbon CCHP system and its comprehensive optimization for an integrated medical and nursing complex

As the global population ages, the demands imposed by medical treatment and care for the elderly are becoming increasingly pressing. To reduce energy consumption and systemic defects in the process of integrating medical treatment and care for the elderly, the authors of this study designed a highly efficient, flexible and low-carbon combined cooling, heating, and power (CCHP) system for the integration of medical and nursing complexes by using a multi-level model of optimization. The complex was modeled in DeST software to obtain the cooling and heating loads, and the general algebraic modeling system software and mixed-integer linear programming (MILP) were used for the collaborative optimization of the choice of equipment, capacity allocation, and operational strategy. The results showed that the proposed system could reduce the total consumption of electric power by 40.95 % when a semiconductor wall was installed on floors 8–12 of the complex for elderly care. The energy management strategy of the system was also optimized by using the vehicle-to-building subsystem in a novel wind–solar–storage and heat pump-based combined cooling, heating, and power system. The rates of reduction in emissions of CO2 and NOx, and the rate of reduction in primary energy consumption of the Wind–Solar–Storage and Heat Pump (WSSH)–CCHP system were 96.5 %, 99.1 %, and 95.6 %, respectively. Finally, the heat pump-assisted liquid-gap membrane distillation (HP-LGMD) subsystem were optimized in MATLAB. The resulting gained output ratio, flux, thermal efficiency of the system, and energy consumed for water production were 0.0269, 4.22 kg/m2, 17.07 %, and 46.88 %, respectively. The proposed system is highly energy efficient, is capable of economical scheduling, and can use energy cascading, which provides guidance on sustainable development of environment and energy.

Optimal Siting and Sizing of FACTS in Distribution Networks Using the Black Widow Algorithm

The problem regarding the optimal placement and sizing of different FACTS (flexible alternating current transmission systems) in electrical distribution networks is addressed in this research by applying a master–slave optimization approach. The FACTS analyzed correspond to the unified power flow controller (UPFC), the thyristor-controlled shunt compensator (TCSC, also known as the thyristor switched capacitor, or TSC), and the static var compensator (SVC). The master stage is entrusted with defining the location and size of each FACTS device using hybrid discrete-continuous codification through the application of the black widow optimization (BWO) approach. The slave stage corresponds to the successive approximations power flow method based on the admittance grid formulation, which allows determining the expected costs of the energy losses for a one-year operation period. The numerical results in the IEEE 33-, 69-, and 85-bus grids demonstrate that the best FACTS device for locating in distribution networks is the SVC, given that, when compared to the UPFC and the TCSC, it allows for the best possible reduction in the equivalent annual investment and operating cost. A comparative analysis with the General Algebraic Modeling System software, with the aim to solve the exact mixed-integer nonlinear programming model, demonstrated the proposed BWO approach’s effectiveness in determining the best location and size for the FACTS in radial distribution networks. Reductions of about 12.63% and 13.97% concerning the benchmark cases confirmed that the SVC is the best option for reactive power compensation in distribution grids.

Efficient Reallocation of BESS in Monopolar DC Networks for Annual Operating Costs Minimization: A Combinatorial-Convex Approach

This article deals with the solution of a mixed-integer nonlinear programming (MINLP) problem related to the efficient reallocation of battery energy storage systems (BESS) in monopolar direct current (DC) grids through a master–slave optimization approach. The master stage solves the integer nature of the MINLP model, which is related to the nodes where the BESS will be located. In this stage, the discrete version of the vortex search algorithm is implemented. To determine the objective function value, a recursive convex approximation is implemented to solve the nonlinear component of the MINLP model (multi-period optimal power flow problem) in the slave stage. Two objective functions are considered performance indicators regarding the efficient reallocation of BESS in monopolar DC systems. The first objective function corresponds to the expected costs of the annual energy losses, and the second is associated with the annual expected energy generation costs. Numerical results for the DC version of the IEEE 33 bus grid confirm the effectiveness and robustness of the proposed master–slave optimization approach in comparison with the solution of the exact MINLP model in the General Algebraic Modeling System (GAMS) software. The proposed master–slave optimizer was programmed in the MATLAB software. The recursive convex solution of the multi-period optimal power flow problem was implemented in the convex discipline tool (CVX) with the SDPT3 and SEDUMI solvers. The numerical reductions achieved with respect to the benchmark case in terms of energy loss costs and energy purchasing costs were 7.2091% and 3.2105%, which surpassed the results reached by the GAMS software, with reductions of about 6.0316% and 1.5736%.

Optimal Power Dispatch of PV Generators in AC Distribution Networks by Considering Solar, Environmental, and Power Demand Conditions from Colombia

This paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consideration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO2 emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids.

Efficient Integration of PV Sources in Distribution Networks to Reduce Annual Investment and Operating Costs Using the Modified Arithmetic Optimization Algorithm

The optimal integration of photovoltaic generation systems is a challenge for distribution utilities since these devices have a direct impact on company finances due to the large amount of investment required at the beginning of the planning project. In this investigation, the problem regarding the optimal siting and sizing of photovoltaic resources in medium-voltage levels is addressed from an economical point of view, where the optimization model that represents said problem corresponds to a mixed-integer nonlinear programming model. The maximum allowed size for single photovoltaic units in the distribution network is set at 2400 kW. The investment costs, energy purchase costs and maintenance costs for photovoltaic units, are considered in the objective function. Typical constraints such as power balance, generation capacities, voltage regulation, among others, are considered in the mathematical formulation. The solution of the optimization model is addressed by implementing a modified version of the Arithmetic Optimization Algorithm, which includes a new exploration and exploitation characteristic based on the best current solution in iteration t, i.e., xbestt. This improvement is based on a Gaussian distribution operator that generates new candidate solutions with the center at xbestt, which are uniformly distributed. The main contribution of this research is the proposal of a new hybrid optimization algorithm to solve the exact optimization model, which is based on a combination of the Arithmetic Optimization algorithm with the Vortex Search algorithm and showed excellent numerical results in the IEEE 34-bus grid. The analysis of quantitative results allows us to conclude that the strategy proposed in this work has a greater effectiveness with respect to the General Algebraic Modeling System software solvers, as well as with metaheuristic optimizers such as Genetic Algorithms, the Newton–Metaheuristic Algorithm, and the original Arithmetic Optimization Algorithm. MATLAB was used as a simulation tool.

Manager perceptions of decision-making for evaluation indicators: a centralized data envelopment analysis based approach

PurposeThis paper aims to propose an integrated centralized data envelopment analysis (CDEA)-balanced scorecard (BSC) model to provide a selective approach to determine the most efficient indicators for evaluating the four perspectives of the BSC.Design/methodology/approachAn integer linear programming model based on the efficiency concept of the CDEA method is presented to select the best indicators for evaluating four perspectives of the BSC. The basis for selecting indicators in this method is to maximize the overall performance of each BSC perspective. The modeling is performed on a real case. The considered model is solved using a general algebraic modeling system software for the data set of the real case.FindingsA real-world case is solved using the proposed method. The integration of the CDEA and the BSC seems to be advantageous because it sheds more light on the complexity and tradeoffs inherent in actual performance measurement. It is important to note that there cannot be a unique and universal model of performance measurement applicable in every situation, in every organization and at any time.Research limitations/implicationsThe data set of a single organization in the manufacturing industry is used to show the performance of the proposed mathematical model; therefore, generalization of the results should be done cautiously. This framework is based on the Iranian community and experts’ viewpoints; therefore, different results may be obtained if it is applied elsewhere, and the importance of perspectives and their indicators might show different results in other populations and other countries. In addition, because the data is collected in a specific period of time, the results cannot be extended to other periods of time.Originality/valueThe main contribution of this paper lies in the adaption of a new integrated CDEA-BSC model to performance measurement in the industrial sector that technology improves the ultimate results of performance measurement and provides wider opportunities for decision-makers. This paper aids managers and decision-makers to control the efficient indicators in perspectives.

Annual Operating Costs Minimization in Electrical Distribution Networks via the Optimal Selection and Location of Fixed-Step Capacitor Banks Using a Hybrid Mathematical Formulation

The minimization of annual operating costs in radial distribution networks with the optimal selection and siting of fixed-step capacitor banks is addressed in this research by means of a two-stage optimization approach. The first stage proposes an approximated mixed-integer quadratic model to select the nodes where the capacitor banks must be installed. In the second stage, a recursive power flow method is employed to make an exhaustive evaluation of the solution space. The main contribution of this research is the use of the expected load curve to estimate the equivalent annual grid operating costs. Numerical simulations in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of the proposed methodology in comparison with the solution of the exact optimization model in the General Algebraic Modeling System software. Reductions of 33.04% and 34.29% with respect to the benchmark case are obtained with the proposed two-stage approach, with minimum investments in capacitor banks. All numerical implementations are performed in the MATLAB software using the convex tool known as CVX and the Gurobi solver. The main advantage of the proposed hybrid optimization method lies in the possibility of dealing with radial and meshed distribution system topologies without any modification on the MIQC model and the recursive power flow approach.

Robust stochastic optimal operation of an industrial building including plug in electric vehicle, solar‐powered compressed air energy storage and ice storage conditioner: A case study in the city of Kaveh, Iran

An optimal day-ahead operation of a microgrid (MG) based on an energy hub (EH) that is an industrial building, is presented in this paper. The proposed EH includes wind turbine (WT), photovoltaic (PV), triple generation that is combined cooling, heat and power, and salt water desalination. The purpose of solving problem is to lessen the operational and pollution costs limited to several technical restrictions. The EH takes into account plug in electric vehicle (PEV) and an ice storage conditioner (ISC) and together with a thermal energy storage system that is a supplementary energy storage system (ESS). Particularly, the performance and efficacy of the EH operational and pollution costs are studied by considering a solar--powered compressed air energy storage (SPCAES) that is a novel rechargeable and developing ESS. The proposed model takes into account the uncertain behaviour of PV and WT generations together with the thermal, electrical, and cooling demands, which deal with a robust optimisation approach. The suggested robust mix integer linear problem model is figured out using the CPLEX solver in general algebraic modelling system software. The proposed framework is implemented on the industrial building located in the industrial city of Kaveh, Iran. The simulation results show that using ESSs including SPCAES, ISC, and PEVs reduce the total costs (operation and emission costs) by 2.42% in the day-ahead energy management.

Optimal Integration of Dispersed Generation in Medium-Voltage Distribution Networks for Voltage Stability Enhancement

This study addresses the problem of the maximization of the voltage stability index (λ-coefficient) in medium-voltage distribution networks considering the optimal placement and sizing of dispersed generators. The problem is formulated through a mixed-integer nonlinear programming model (MINLP), which is solved using General Algebraic Modeling System (GAMS) software. A numerical example with a 7-bus radial distribution network is employed to introduce the usage of GAMS software to solve the proposed MINLP model. A new validation methodology to verify the numerical results provided for the λ-coefficient is proposed by using recursive power flow evaluations in MATLAB and DigSILENT software. The recursive evaluations allow the determination of the λ-coefficient through the implementation of the successive approximation power flow method and the Newton–Raphson approach, respectively. It is effected by fixing the sizes and locations of the dispersed sources using the optimal solution obtained with GAMS software. Numerical simulations in the IEEE 33- and 69-bus systems with different generation penetration levels and the possibility of installing one to three dispersed generators demonstrate that the GAMS and the recursive approaches determine the same loadability index. Moreover, the numerical results indicate that, depending on the number of dispersed generators allocated, it is possible to improve the λ-coefficient between 20.96% and 37.43% for the IEEE 33-bus system, and between 18.41% and 41.98% for the IEEE 69-bus system.

Designing a locating-routing three-echelon supply chain network under uncertainty

PurposeIn the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.Design/methodology/approachBy adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.FindingsAccording to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.Research limitations/implicationsSince this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.Practical implicationsThe suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.Originality/valueAccording to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

Economy-wide Impacts of Tariff Cuts and Productivity Improvements in Agriculture: A Computable General Equilibrium (CGE) Analysis for Sri Lanka

This study provides a quantitative assessment of the likely economy-wide impacts of tariff cuts and productivity improvements in agriculture for Sri Lanka. A static, multisector, Computable General Equilibrium (CGE) model, using the Supply and Use Table (SUT) of 2010, was employed highlighting specified agricultural sub-sectors and their interactions with other production sectors in the economy. Constructing a CGE model entailed the development of a Social Accounting Matrix (SAM) to represent the Sri Lankan economy. The SUT, household income and expenditure survey, economic stat of the Department of Census and Statistics, and economic data library of the Central Bank of Sri Lanka were used to develop the SAM. The SAM was used to calibrate the CGE model. Coding and operationalization of the CGE model were executed using the PATH solver of the General Algebraic Modeling System software using a modified version of the standard CGE model. Production was specified as a Constant Elasticity of Substitution (CES) production function whereas consumption was specified as a Linear Expenditure System (LES). Using the HIES data, the LES was estimated using a seemingly unrelated regression model. The CGE model included a representative household, two factors of production <em>i.e.</em>, labor and capital, commodities, activities, the government, savings, and investment and trade. Productivity improvements in the selected agricultural subsectors lead to a significant positive response in the paddy, vegetables, coconut growing, and livestock sectors. However, productivity improvements in the specified agricultural sectors lead to a decline in demand for labor because of improved primary factor productivity and the decline of market prices. A cut in prevailing tariffs in agricultural industries shows negative impacts on households as a whole.

Optimizing A Fuzzy Multi-Objective Closed-loop Supply Chain Model Considering Financial Resources using meta-heuristic

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.

A Cost-Emission-Based Multi-objective Dynamic Economic Dispatch Considering Solar-Wind Curtailment Cost

A dependable and cost-effective energy distribution scheme is the need in the current scenario of distributed generation of electricity. In the recent past, different economic dispatch techniques are proposed to address this challenge of cost-effectiveness. The fossil fuels issue of pollutant emission and the limited stock has pushed the research community to focus on the direction of renewable energy generation. The aim of this paper is to present a multi-objective cost-emission-based dynamic economic dispatch (MOCEDED) algorithm, which includes thermal, solar, and wind-based power plants. Moreover, the uncertainty of solar and wind power in terms of curtailment is also included in the formulation of the algorithm. The formulated objective function is quadratic constrained programming, which is implemented in General Algebraic Modeling System software. Two paradigms, i.e. only cost-based DED and MOCEDED algorithm, have been analyzed. The outcome of the proposed MOCEDED algorithm shows more promising results than cost-based DED.

Dynamic Reactive Power Compensation in Power Systems through the Optimal Siting and Sizing of Photovoltaic Sources

The problem of the optimal placement and sizing of photovoltaic power plants in electrical power systems from high- to medium-voltage levels is addressed in this research from the point of view of the exact mathematical optimization. To represent this problem, a mixed-integer nonlinear programming model considering the daily demand and solar radiation curves was developed. The main advantage of the proposed optimization model corresponds to the usage of the reactive power capabilities of the power electronic converter that interfaces the photovoltaic sources with the power systems, which can work with lagging or leading power factors. To model the dynamic reactive power compensation, the η-coefficient was used as a function of the nominal apparent power converter transference rate. The General Algebraic Modeling System software with the BONMIN optimization package was used as a computational tool to solve the proposed optimization model. Two simulation cases composed of 14 and 27 nodes in transmission and distribution levels were considered to validate the proposed optimization model, taking into account the possibility of installing from one to four photovoltaic sources in each system. The results show that energy losses are reduced between 13% and 56% as photovoltaic generators are added with direct effects on the voltage profile improvement.

Development of a non-dominated sorting genetic algorithm for implementing circular economy strategies in the concrete industry

Abstract The concrete manufacturing supply chain is one of the most carbon-intensive systems in the construction industry. Decision-making based on a multi-objective approach that accommodates the environmental impacts of concrete manufacturing has been rarely investigated. To fill this gap, a sustainable closed-loop supply chain (SCLSC) model was conceived to capture the effect of different circular systems. The proposed model consists of different sub-systems including customers, suppliers, and manufacturing and recycling stations. An evolutionary-based algorithm named non-dominated sorting genetic algorithm was incorporated to solve the solutions. The SCLSC model was implemented on two scenarios based on in-house or outside recycling plants to minimize the quarry of natural resources, the transportation cost, and the greenhouse gas (GHG) emissions. General Algebraic Modeling System (GAMS) programming software was used to validate the model and to verify the obtained results from the model. The Pareto solution results show that larger incorporation of recycled aggregates in concrete production can lower the excavation of quarries. Furthermore, although a decision for incorporating a green (net-zero GHG) cement contributes to reducing the GHG emissions of the supply chain, the transportation distance determines whether the demand would be supplied from the green cement source. The provision of an in-house concrete recycling unit can reduce the GHG emissions of the supply chain by 14% while the cost and virgin aggregate demand increase by 24% and 16%, respectively compared to a system that has an outside recycling plant. The validation process shows that the GAMS software needs additional steps to conduct the multi-objective Pareto solutions. Similar values were obtained from the proposed algorithm compared to the outcome of GAMS. The computational results prove the capability of the proposed SCLSC model and the applicability of the implemented solution approach. This approach can facilitate the decision-making process due to its effectiveness in improving customers’ economic motivations and in reducing the environmental impacts.

Coordinated Operational Optimization Approach for PV Inverters and BESSs to Minimize the Energy Loss of Distribution Networks

The article presents a coordinated operational optimization approach for PV inverters and battery energy storage systems (BESSs) to minimize the energy loss in distribution networks with time-varying load demand and PV generation. PV inverters are modeled to inject available PV generation into a network during PV generation hours and to provide VAr compensation during rest of the time. BESSs are modeled to store surplus PV power during high penetration of PV generation and to discharge stored energy during no/low PV generation hours. An optimization problem is formulated to determine the optimal VAr compensation setpoints for PV inverters and optimal charge/discharge schedule for the BESSs. The maximization of PV hosting capacity and the minimization of energy loss are the objective functions for the first and second stages, respectively. The limits on bus voltage magnitude, line flow, state-of-charge (SOC) of BESSs, charging and discharging of BESSs, PV inverter capacity, etc., are considered to be the operational constraints. The CONOPT solver of general algebraic modeling system software is used for solving the optimization problem. The contribution of the proposed approach is to show that significant energy loss reduction can be obtained with the optimal time-varying setpoints of PV inverters and BESSs.

A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders

This paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations through a branch-to-node incidence matrix. The general algebraic modelling system (GAMS) is chosen as a solution tool, showing in tutorial form the implementation of the proposed MINLP model in a 6-nodes test feeder with 10 candidate lines. The validation of the MINLP formulation is performed in two classical 10-nodes DC test feeders. These are typically used for power flow and optimal power flow analyses. Numerical results demonstrate that power losses are reduced by about 16% when the optimal reconfiguration plan is found. The numerical validations are made in the GAMS software licensed by Universidad Tecnológica de Bolívar.

Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

Integrated energy hub system based on power‐to‐gas and compressed air energy storage technologies in the presence of multiple shiftable loads

Integrated energy carriers in the framework of energy hub system (EHS) have an undeniable role in reducing operating costs and increasing energy efficiency as well as the system's reliability. Nowadays, power-to-gas (P2G), as a novel technology, is a great choice to intensify the interdependency between electricity and natural gas networks. The proposed strategy of this study is divided into two parts: (i) a conditional value-at-risk-based stochastic model is presented to determine the optimal day-ahead scheduling of the EHS with the coordinated operating of P2G storage and tri-state compressed air energy storage (CAES) system. The main objective of the proposed strategy is to indicate the positive impact of P2G storage and tri-state CAES on lessening the system uncertainties including electricity market price, power generation of the wind turbine, and even electrical, gas, and thermal demands. (ii) A demand response program focusing on day-ahead load shifting is applied to the multiple electrical loads according to the load's activity schedule. The proposed strategy is successfully applied to an illustrative example and is solved by general algebraic modeling system software. The obtained results validate the proposed strategy by demonstrating the considerable diminution in the operating cost of the EHS by almost 4.5%.