A fuel-constrained unit commitment method

The authors present a thermal unit commitment method for interconnected multiarea power systems. The method is a natural extension of the commitment utilization factor (CUF)-based single-area unit commitment method. CUF is extended to reflect the impact of multiarea transmission interconnection constraints on the utilization efficiency of committing a candidate unit at a given priority position. The multiarea CUF is used in conjunction with the average full load cost. (AFLC) to efficiently determine the optimal or a near-optimal multiarea priority commitment order. This priority order is then used to generate the multiarea unit commitment schedule. The excellent quality of the results of the CUF-based multiarea method is consistent with that of the previously reported single-area method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Unit commitment is an efficient method to lower the total cost of the power plant by scheduling units' generation and meeting the constraint condition. However, in practice, few power plants implement the algorithm for fear that the load response in the power plant would not be fast enough to track the unscheduled load demand from the power grid if some units of the power plant are shut down. This paper proposes a new unit commitment method that tries to solve this problem effectively. The proposed method guarantees the desired speed limit by setting the plant-level load tracking speed. In this method a ‘one-at-a-time’ unit commitment is accomplished. The most expensive unit (which is determined by computing its cost) is optimized, the unit commitment schedule is updated, and the load dispatch result is re-computed. The result will be repeatedly optimized until the spinning reserve or load tracking speed is lack. The optimization loop will breaks out as well if the unit schedules of two consecutive iterations over the time period remain unchanged. The test results from 10-machine system shows that the proposed method is easy to understand and quick to solve, and is of practical use.

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

A thermal unit commitment (UC) computer program has been used at the Riyadh Energy Control Centre of Saudi Consolidated Electric Company, Central Region, (SCECO Central) since 1982. Since then, system growth, plant aging, turbine degradation, ambient temperature effect on unit output, transmission constraints (line loading), MVAr/voltage control problem at light loads, and stringent operating requirements (spinning reserve, etc.) have imposed many constraints on the program, making it nearly impossible to produce a generation allocation schedule that is acceptable to system operators. A study undertaken to decide what should be done with the UC program and how the operator could be supplied with a daily generation schedule that is sufficiently accurate and practically feasible is described. Because it was not feasible to provide a new unit commitment program or to modify the existing one, it was decided to tailor the unit commitment input dataset such that the constraints are appropriately represented. In addition, it was decided to write a suite of computer programs (UCPROG) in FORTRAN that would process necessary input data and results of the UC program to achieve a more realistic, detailed, but precise unit allocation schedule.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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

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.

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.

Unit commitment is a complex problem which, until now and for real size systems, has been solved using heuristic methods only. This paper proposes a new Lagragian relaxation method, which produces ??-optimal solutions. The flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.

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.

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.

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.

Enhanced priority list unit commitment method for power systems with a high share of renewables

Concerning the realistic situation of power system in China, a novel unit commitment method is presented which takes comprehensive consideration for the coordinative operation of wind energy, demand response and thermal power. The proposed day-ahead scheduling scheme is aimed at practical utilization in current grid foundation. Particularly, this paper takes a practical consideration for the actual electricity consumption of electrical vehicles in China on the side of clients. A price mechanism based on power consumption contract of electrical vehicles (EVs) is proposed so that customers could regulate their charging behaviors towards an EV-friendly type. Moreover, a ten-level set of demand response is used and the uncertainty of wind power generation is regulated by a polyhedron set. The optimization model is built on an IEEE 114-bus system with 5 units, which is then solved by a real-code genetic algorithm. As shown in the numerical study, the proposed model can provide specific and economic beneficial dispatching schemes for system operators as well regulate power consumption of EVs, which effectiveness has been verified.

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