Profit Based Unit Commitment Problem Research Articles

Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method

The unit commitment (UC) problem aims to reduce the power generation costs of power generation units in the traditional power system structure. However, under the current arrangement, the problem of cutting the cost of producing electricity has turned into an opportunity to boost power generation units’ profits. Emission concerns are now given considerable weight when talking about the performance planning of power generation units, in addition to economic objectives. Because emissions are viewed as a limitation rather than an objective function in the majority of recent research that has been published in the literature, this paper solves the multi-objective profit-based unit commitment (PBUC) problem while taking into account energy storage systems (ESSs) and renewable energy systems (RESs) in the presence of uncertainty sources, such as demand and energy prices, in order to minimize generated emissions and maximize profits by power generation units in the fiercely competitive energy market. Owing to the intricacy of the optimization problem, a novel mutation-based modified version of the shuffled frog leaping algorithm (SFLA) is suggested as a way to get around the PBUC problem’s difficulty. A 10-unit test system is used for the simulation, which is run for a whole day to demonstrate the effectiveness of the suggested approach. The proposed algorithm’s output is compared with the best-known approaches from various references. The simulated results generated by the suggested algorithms and the previously reported algorithms to solve the PBUC problem show that the proposed method is better than other evolutionary methods utilized in this study and prior investigations. For example, the overall profit from the suggested MSFLA is around 4% and 5.5% higher than that from other algorithms like the ICA and Muller methods in the presence and absence of reserve allocation, respectively. Furthermore, the MSFLA emissions value is approximately 2% and 8% lower than the optimum emissions values obtained using the PSO and ICA approaches, respectively.

Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market

Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market

Memetic binary differential evolution to solve wind–thermal profit based unit commitment problem

Recently, the utilization of renewable energy sources (RESs) has gained popularity all over the world due to its numerous benefits. The RESs are a good choice as a cleaner power producer with economic benefits. Power generation from wind energy is rapidly growing all over the world as compared to other RESs. Literature review reveals that a combinatorial, mixed integers (binary and real) optimization problem also known as profit based unit commitment problem (PBUCP) has been solved for only thermal generating units without using RESs. These days, the PBUCP with the integration of RESs has gained intention. In this paper, the PBUCP is solved with the integration of wind generating units in a single objective framework, wherein objective is profit maximization, as well as multi-objective framework, wherein objectives are profit maximization and emissions minimization. A memetic binary differential evolution (MBDE) algorithm is proposed to solve the wind–thermal​ PBUCP. The proposed MBDE algorithm is a blending of binary differential evolution (BDE) and binary search optimizer (BSO). The proposed hybridization improves the global searching capability of the BDE algorithm while maintaining the effective exploitation capability of the BSO. The proposed BSO makes binary perturbations to ameliorate the performance of the BDE algorithm. The achievement of the proposed algorithms has been tested on two hybrid power systems that comprise wind generating units and thermal generating units. In order to know the impacts of wind power generation on thermal power generation, the results of the wind–thermal PBUCP are compared with the results of the thermal PBUCP in the single and multi-objective framework. The best results of the proposed algorithms are collated with the best results of already applied well known algorithms and are found superior.

Profit based unit commitment problem solution using metaheuristic optimisation approach

Earlier power utilities had generated electrical power with the goal of minimising the costs and meeting both demand and reserves. Generation companies (GENCOs) schedule their generators under a new structure to maximise the profit. This paper provides a computational methodology based on monarch butterfly optimisation (MBO) to find a solution to the problem of profit-based unit commitment (PBUC), taking into account the power and reserve conditions. The proposed method allows GENCOs to determine how much power and reserve should be sold in the markets in order to achieve maximum profit. The binary variables of unit commitment problems are handled by modifying the continuous-time nature of the monarch butterfly algorithm. The computational approach maximises the profit but also handles the mixed constraints of the commitment problem. The effect of thermal turbine valve point loading is also taken into consideration. The computational technique has been used to solve two test systems in the absence and presence of steam turbine valve point loading effects. The results obtained are in agreement with the recent results available in the literature. Comparative analysis shows the effectiveness of the proposed MBO based solution methodology in terms of profit earned and execution time in relation to other techniques.

Optimal Unit Commitment with Renewable Energy in Regulated and Deregulated Systems

In this paper, the impact of the wind power generation system on the total cost and profit of the system is studied by using the proposed procedure of binary Sine Cosine (BSC) optimization algorithm with optimal priority list (OPL) algorithm. As well, investigate the advantages of system transformation from a regulated system to a deregulated system and the difference in the objective functions of the two systems. The suggested procedure is carried out in two parallel algorithms; The goal of the first algorithm is to reduce the space of searching by using OPL, while the second algorithm adjusts BSC to get the optimal economic dispatch with minimum operation cost of the unit commitment (UCP) problem in the regulated system. But, in the deregulated system, the second algorithm adopts the BSC technique to find the optimal solution to the profit-based unit commitment problem (PBUCP), through the fast of researching the BSC technique. The proposed procedure is applied to IEEE 10-unit test system integrated with the wind generator system. While the second is an actual system in the Egyptian site at Hurghada. The results of this algorithm are compared with previous literature to illustrate the efficiency and capability of this algorithm. Based on the results obtained in the regulated system, the suggested procedure gives better results than the algorithm in previous literature, saves computational efforts, and increases the efficiency of the output power of each unit in the system and lowers the price of kWh. Besides, in the deregulated system the profit is high and the system is more reliable.

GAMS Environment Based Solution Methodologies for Ramp Rate Constrained Profit Based Unit Commitment Problem

GAMS Environment Based Solution Methodologies for Ramp Rate Constrained Profit Based Unit Commitment Problem

A synergy of binary differential evolution and binary local search optimizer to solve multi-objective profit based unit commitment problem

Environmentally constrained profit based unit commitment problem (PBUCP) is a combinatorial multi-objective optimization problem experienced in deregulated power systems, which optimizes ON/OFF status of generating units to maximize economic benefits with less environmental impacts. The PBUCP has gained less attention in the multi-objective framework. The Kyoto Protocol and various incentive policies have forced generation companies to consider emission minimization as an objective function while solving the PBUCP. In most of the recent studies in the literature, emissions are considered as a constraint, not as an objective function. A few studies have considered the emissions as an objective and suggested the compromised solutions of the multi-objective PBUCP. A synergy of the binary differential evolution (BDE) algorithm and a binary local search optimizer (BLSO) is proposed in the present research to solve the multi-objective PBUCP. A novel BLSO is proposed which deals with binary variables while exploiting the local search and is implemented on the commitment part of the multi-objective PBUCP. The proposed BLSO makes perturbations in the unit status based on the priority of units to refine the optimal solution searched by the BDE algorithm. The efficacy of proposed algorithms has been investigated on the small, medium and large power systems. A comparison with previously known best solutions is performed for the validation of obtained results. Computed results of proposed algorithms are dominant than the already published algorithms applied to solve the multi-objective PBUCP.

Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units

With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.

A mutual approach for profit-based unit commitment in deregulated power system integrated with renewable energy sources

This paper proposes a mutual technique for solving the profit-based unit commitment (PBUC) problem in deregulated power system integrated with wind power. The proposed mutual approach is the joined execution of different solution techniques and known by the non-dominated sorting of moth fly optimization (MFO) with levy flight search (NSMFLF) technique. In the proposed approach, the levy flight search and the traditional moth flame optimization looking conduct is prepared in parallel as for the objective function and update the conceivable combination of generation units. The objective function maximizes the profit of the generating companies as for the revenue and total fuel cost in light of the gauge estimations of power demand, price and reserve power. Here, the uncertainty events of the wind power are predicted by utilizing the artificial intelligence techniques. Thus, the system is ensured with the high utilization of wind power. Finally, the non-dominated sorting is performed to choose the optimal solution from the conceivable generated combinations. The optimal combination used to maximize the profit of the generating companies and solve the PBUC problem in light of the objective function. The proposed method is implemented in the matrix laboratory working stage and the outcomes are analyzed with the current strategies.

Profit Based Unit Commitment of Thermal Units with Renewable Energy and Electric Vehicles in Power Market

In restructured power system, Generation Companies (GENCOs) has an opportunity to sell power and reserve in power market to earn profit by market clearing process. Defining unit commitment problem in a competitive environment to maximize the profit of GENCOs while satisfying all the network constraints is called Profit Based Unit Commitment problem (PBUC). The main contribution of this paper is modeling and inclusion of Market Clearing Price (MCP) in PBUC problem. In Day market, MCP is determined by market operator which provides maximum social welfare for both GENCOs and Consumers.On other hand this paper proposes a novel combination of solution methodology: Improved Pre-prepared power demand (IPPD) table and Analytical Hierarchy method (AHP) for solving the optimal day ahead scheduling problem as an another contribution. In this method, the status of unit commitment is obtained by IPPD table and AHP provides an optimal solution to PBUC problem. Minimizing total operating cost of thermal units to provide maximum profit to GENCOs is called an optimal day ahead scheduling problem. Also it will be more realistic to redefine this problem to include multiple distributed resources and Electric vehicles with energy storage. Because of any uncertainties or fluctuation of renewable energy resources (RESs), Electric vehicles (EV) can be used as load, energy sources and energy storage. This would reduce cost, emission and to improve system power quality and reliability. So output power of solar (PS), wind output power (PW) and Electric Vehicles power (PEV) are modeled and included into day ahead scheduling problem.The proposed methodology is tested on a standard thermal unit system with or without RESs and EVs. Cost and emission reduction in a smart grid by maximum utilization of EVs and RESs are presented in this literature. It is indicated that the proposed method provides maximum profit to GENCOs when compared to other methodologies such as Memory Management Algorithm, Improved Particle Swarm Optimization (PSO), Muller method, Gravitational search algorithm etc.

Dust effect impact on PV in an aggregation with wind and thermal powers

Abstract This paper is about the dust effect impact on photovoltaic systems on the profit of an electricity market agent acting as an aggregator of photovoltaic power, wind power, thermal power, and an energy storage system. Energy storage ensures arbitrage and smoothing of the variability of photovoltaic power and wind power. The market agent intends to derive bids for submission in a day-ahead market, having consideration of the dust effect impact on the photovoltaic power. A formulation is proposed for a support decision system by a profit-based unit commitment problem solved by a stochastic programming approach, considering the operating characteristics of the virtual power plant. The photovoltaic power, wind power, and market price uncertainties are input data derived from scenarios of historical data. Case studies addressed show the advantages of the stochastic programming approach and insights concerned with the integration of uncertainties within the modeling for the schedule of the energy storage system and the dust effect impact on profit.

Profit based Unit Commitment Problem solving using Hybrid CUCKOO- GWO Algorithm

This paper proposes a hybrid approach using cuckoo search ahead with GWO to resolve PBUCP for a restructured power market. Under this structure, the issues in profit are improved using objective functions such as maximize profit using revenue and the total cost of the system and system constraints such as power demand constraints, reserve constraints, generator, and reserve limits and a minimum up and downtimes are determined. Here, two types of test systems such as IEEE-30 bus and IEEE-75 bus system is utilized. After the determination of the above objective functions, a hybrid method called Cuckoo- GWO algorithm is used for optimization. The simulations are done to demonstrate the performance of the proposed technique. At last, the comparison of the proposed and existing algorithms is evaluated in terms of profit and total operating cost for IEEE 30 bus system of 6-units and IEEE 75 bus system of 15-units respectively.

A New Solution to Profit Based Unit Commitment Problem Considering PEVs/BEVs and Renewable Energy Sources

Daily load demand for industrial, residential and commercial sectors are changing day by day. Also, inclusion of e-mobility has totally effected the operations of realistic power sector. Hence, to meet this time varying load demand with minimum production cost is very challenging. The proposed research work focuses on the mathematical formulation of profit based unit commitment problem of realistic power system considering the impact of battery electric vehicles, hybrid electric vehicles and plug in electric vehicles and its solution using Intensify Harris Hawks Optimizer (IHHO). The coordination of plants with each other is named as Unit commitment of plants in which the most economical patterns of the generating station is taken so as to gain low production cost with higher reliability. But with the increase in industrialization has affected the environment badly so to maintain the balance between the generation and environment a new thinking of generating low cost power with high reliability by causing less harm to environment i.e. less emission of flue gases is adopted by considering renewable energy sources.

Profit based unit commitment using memetic binary differential evolution algorithm

Profit-based unit-commitment problem (PBUCP) is a notable combinatorial optimizing problem faced in the deregulated power industry. The PBUCP finds the best profitable solution by committing and scheduling the thermal generating units efficiently. To solve the PBUCP, a new memetic binary differential evolution algorithm is proposed which considers binary differential evolution (BDE) algorithm as global search operator to improve the exploration aspect and binary hill-climbing (BHC) algorithm as local search operator to improve the exploitation aspect. A binary differential evolution algorithm is introduced whereby a new mutation strategy is implemented. A novel BHC algorithm makes priority-based perturbations on unit’s status to improve the global best solution searched by the BDE algorithm alone. A new excessive unit de-commitment strategy based on priority and total profit is also proposed. The power to committed units is allocated based on priority of units. The efficacy of algorithms has been researched on the PBUCP test systems comprising of 10-, 40- and 100-units over a time horizon. The outcomes of the proposed algorithms are compared with previously known best solutions. Simulated outcomes achieved by the proposed algorithms compete with the already reported algorithms to solve the PBUCP. Wilcoxon signed-rank test proves the predominance of the proposed algorithms statistically.

Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market

Abstract The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches.

APPLICATION OF AHP METHOD FOR SOLVING THE UNIT COMMITMENT PROBLEM IN A DAY AHEAD MARKET

The aim of unit commitment problem in power systems has been converted from cost minimization to profit maximization with the liberalization of power markets. The generation companies (GENCOs) schedule the units to maximize their profit for the forecasted prices in day ahead market (DAM). The generation scheduling of generators in deregulated environment is called as Profit Based Unit Commitment (PBUC). In this paper, an application of Analytic Hierarchy Process (AHP) is proposed to solve PBUC problem. The method is applied to 3-units power system. The results are compared with the methods in the literature. As shown in the study, the proposed AHP method introduces its applicability and efficiency for solving the unit commitment problem in a day ahead market.

Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

ABSTRACTThis article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.

A hybrid LR-secant method-invasive weed optimisation for profit-based unit commitment

This paper proposes a hybrid Lagrangian relaxation (LR)-secant method-invasive weed optimisation (IWO) for solving profit-based unit commitment (PBUC) problem. The PBUC problem is one of the important optimisation problems in deregulation. The objective of generation companies (GENCOs) in deregulated environment is to schedule the generating units for maximising the profit. Three stages are involved in the suggested method. The unit commitment problem is solved by Lagrangian relaxation (LR) for a given forecasted power demand, reserve and electricity price by incorporating various constraints, then optimal output powers for committed units are determined by secant method and finally IWO is applied to update the Lagrangian multipliers based on the duality gap between the primal cost and duality cost. It has been tested for various test cases such as 3 units, 10 units and 20 units system to prove the applicability of the proposed method. Simulation results have been compared with existing methods available in the literature. A comparison of the simulation results of the recommended method with the results of previous published methods shows that this method provides a qualitative solution.

A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

The profit-based unit commitment (PBUC) procedure is a binary natured operational planning problem of generation company (GENCO) in competitive electricity market. The GENCO solves PBUC problem in day ahead market of energy and reserve markets by commitment and scheduling of thermal units with an objective of profit maximization for the set of given price and load forecasts. Therefore, the solution quality and efficacy of PBUC optimization problem play a vital role in ensuring maximum returns to the GENCO. This paper presents a mathematical model-inspired heuristic approach called binary sine–cosine algorithm (BSCA) for solving PBUC problem. The SCA algorithm is a heuristic approach that uses the fluctuating nature of individual candidates/search agents in the search space around the global solution. The fluctuating mechanism is realized by using mathematical functions, i.e. sine and cosine functions. The proposed binary approach of SCA algorithm uses modified sigmoidal transformation function for binary mapping of continuous real-valued search space to binary counterpart. The efficacy of proposed approach in terms of solution quality and convergence is demonstrated using test system with different market participation policies. The results demonstrate the effectiveness of proposed BSCA approach over existing approaches for solving PBUC.

Hybrid Particle Swarm Optimization Algorithm to solve Profit based Unit Commitment Problem with Emission Limitations in Deregulated Power Market

Hybrid Particle Swarm Optimization Algorithm to solve Profit based Unit Commitment Problem with Emission Limitations in Deregulated Power Market