Solution method for unit commitment limitations and utility constraints

In this paper, first time ever artificial colony bee search is applied for thermal unit commitment. The main aim of doing thermal unit commitment scheduling is to optimize running while ensuring security and reliability. In recent era, lot of attention is given to the environmental conditions. All developed and few developing countries are worrying about emission of carbon dioxide from thermal generating stations. In first section of this paper unit commitment and Artificial Bee Colony (ABC) is discussed. In next section methodology for adopting ABC and general constraints of unit commitment are discussed in great detail. In last section application of ABC for unit commitment is implementation of proposed algorithm is shown and obtained results are presented. It is observed that ABC produces good results for unit commitment problem and easy to implement.

A novel projected two-binary-variable formulation for unit commitment in power systems

ABSTRACTThis paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.

This paper presents a new approach of particle swarm optimization (PSO) algorithm for short term hydro thermal scheduling (HTS) problems. Various possible particle selections have been studied and its effects on the global optima have been discussed. The effectiveness and stochastic nature of proposed algorithm has been tested with standard test case and the results have been compared with earlier works. This paper also describes software developed for short term hydro-thermal scheduling by considering hydro economic dispatch and thermal unit commitment. The proposed algorithm is ideally suitable for hydro-thermal co-ordination problems, hydro economic dispatch problems with unit commitment, thermal economic dispatch with unit commitment problems and scheduling of hydraulically coupled plants.

Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators

The authors present a genetic approach for determining the priority order in the commitment of thermal units in power generation. The objective of the problem is to properly schedule the on/off states of all thermal units in a system to meet the load demand and the reverse requirement at each time interval, such that the overall system generation cost is a minimum, while satisfying various operational constraints. The authors examine the feasiblity of using genetic algorithms and report some simulation results in near-optimal commitment of thermal units in a power system.

Since many countries worldwide are going toward the liberalisation of electric energy markets, the need of forecast tools for unit commitment is growing. These tools allow us determining a reliable forecast for the hourly production and price and use of each generator. These are crucial pieces of information for all the players in the market, which enable to compute lower bounds for expected prices on real markets. Although unit commitment is a classical problem, and many professional or research software products are available for its solution, it is not easy to find a tool that is able to manage systems with a large number of generators, taking into account the dynamic constraints between different hours on a long time horizon, and the transmission constraint between several areas. The proposed methodology for optimal unit commitment has been developed with the purpose of giving a reliable solution for systems with hundreds of generators, radially connected areas. This solution performs commitment and dispatch hour by hour, searching for overall optimal solutions, taking into account the above-mentioned constraints, on a time horizon on 1 year. The algorithm has been tested on a real system, namely the Italian one and final results are produced within a reasonable computation time.

To enhance the resilience of transmission networks with offshore wind farms (OWFs) and pump hydro energy storage systems (PHESSs) towards typhoons, a resilient unit commitment scheme is proposed. This problem is formulated as a two-stage robust optimization problem, where the uncertain impacts of typhoons on OWF output, transmission line failures, and demands are captured by a robust uncertain set with chance constraints. This uncertainty set is embedded into a two-stage robust unit commitment model, where the commitment of thermal units is optimized in the first-stage and PHESSs are scheduled in the second-stage optimization as the emergency resources. The two-stage robust optimization problem is solved using the column and constraint generation (C&CG) algorithm. Simulations are performed on the modified IEEE-RTS test systems with real OWF output during Typhoon Higos, and the effectiveness of proposed scheme is verified regarding the reduction of worst case load shedding.

The paper describes a procedure developed to assist a generating company in choosing the most convenient bidding strategies for a day-ahead electricity energy market. According to the proposed method, the profit maximization problem is transformed into a minimization problem that can be solved by a traditional hydro-thermal unit commitment program after implementing a few modifications. The paper describes the modifications introduced in a unit commitment program based on the Lagrangian relaxation approach and on a disaggregated Bundle method for the solution of the dual problem. It also presents some results obtained for a realistic data set of hydrothermal power plants. The results are discussed in order to emphasize how the method can be applied to assess the bidding strategy choice of a given company.

A short-term fuel-constrained thermal unit commitment method which includes significant constraints such as take-or-pay gas contracts and limitations associated with the gas delivery system is described. The fuel constraints are explicitly considered in determining the short-term (e.g., daily, weekly) thermal unit commitment strategy. The method results from the extension of a novel iterative approach consisting of two phases: a sequential commitment phase and a parameter adjustment phase. The method is effective in both its computational requirements and its solution quality. Illustrative examples are presented. >

This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.

This article presents a strategy to solve thermal unit commitment (UC) integrated with an equivalent solar battery system using Fuzzy based Quantum inspired Evolutionary Algorithm (FQEA). As a renewable power source, solar power is injected stochastically with the model. To handle the uncertainty and intermittency involved while integrating solar power and load forecasting, the trivial crisp problem formulations are modified by fuzzification. An evolutionary algorithm based on the concept and principle of quantum computation is applied to solve the UC problem. The conventional Quantum Evolutionary Algorithm (QEA) is advanced by using several operators such as binary differential operator, mutation and crossover along with trivial rotation operator with a re-defined rotational angle look-up table. The QEA is further modified by introducing multi-population based scheme. The fitness function is formulated by combining the objective function, penalty function and the aggregated fuzzy membership function. The proposed FQEA is applied to UC problem in different scaled power systems up to 100 units. Provided simulation results will show the effectiveness of FQEA.

A fuzzy optimization-based approach to large scale thermal unit commitment

This chapter describes and formulates three important problems for the economic management of power systems, namely, the Unit Commitment (UC), the Economic Dispatch (ED), and the Network Constrained Unit Commitment (NCUC) problems. The UC problem determines the commitment of generating units with the aim of minimizing operating costs while supplying the demand and meeting technical and security constraints. Then, once the commitment of generating units is decided, the ED problem determines the actual power output of each generating unit that is needed to supply all demands at minimum cost, while complying with the technical constraints of the transmission network. Finally, if both the commitment of generating units and transmission constraints are considered simultaneously, the resulting problem is known as the NCUC problem. These three problems are mostly considered in electricity markets throughout the US.

Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC is used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system to increase the saving in the power system by applying the Unit Commitment (UC) to the power system. This work proposes Local Attracting Quantum Particle Swarm Algorithm (LAQPSO) to solve the unit commitment problem in power systems. The local attractor in the LAQPSO algorithm is used to obtain the rotation angle direction and magnitude for updating the quantum angle using the quantum rotation gate. The proposed algorithm is applied to solve the UC problem for a 26 units power system. A comparison with the Binary PSO (BPSO), Improved Quantum BPSO (IQBPSO) and other techniques in the literature was implemented to show the efficiency and the accuracy of the proposed algorithm. The results show the superior performance of the proposed LAQPSO algorithm to minimize the total cost when compared with BPSO, IQBPSO and the literature works.