Short term unit-commitment using genetic algorithms

Unit Commitment is one of the most complex and difficult optimization problem in power systems. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirements at each time period, such that the overall cost of generation is minimized, while satisfying the various operational constraints. This paper presents the application of an improved genetic algorithm (GA) with crossover, mutation, and advanced operators such as repair and swap mutation to determine the commitment order of the thermal units in power generation. The proposed GA has been successfully applied to 10 and 26 generating-unit systems. Robustness of the proposed GA is demonstrated by comparison to the dynamic programming algorithm. The comparison results show the effectiveness of the proposed GA in solving the unit commitment problem with sufficient accuracy and low computational time.

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

This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach. >

Grid-connected mode of microgrid (MG) operation is the most economical and reliable mode of service because the grid participates in the buying and selling of power with the MG, which reduces the generation cost of the MG system. Various types of electricity market pricing strategies are available in the literature with which the grid buys and sells power to the MG system. These strategies are implemented in turns on two different low voltage (LV) MG systems, and the generation cost of the systems are recorded to distinguish the cheapest strategy among them. A novel hybrid of recently developed superior optimization algorithms viz. grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) is used to minimize the overall generation cost of the MG system. It was evident from the results that generation cost was minimum when electricity market pricing was done using Strategy 2. Also, active participation of the grid incurred 15% savings in the system generation cost for the same strategy compared to the case when the grid was operating passively. Furthermore, statistical analysis corroborates to the superiority of the proposed hybrid MGWO-SCA-CSA over other algorithms used.

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 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>

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

The establishment of more close to the actual model is helpful to quickly implement energy saving and emission-reduction. The day-ahead multi-objective optimal dispatching model containing thermal power, hydro power, wind-power and pumped storage units is given to minimize the total costs and CO2 emission under multiple constraints. Considered the longer startup-shutdown period of thermal power units, the unit commitment of thermal units is predetermined according to the minimal operation costs. the improved hybrid particle swarm optimization algorithm are used to solved the model for the minimal total costs and carbon emission, the unit commitment of pumped storage and the power generation of thermal power, hydro power, wind-power and pumped storage units are obtained. The verification is performed through modified IEEE 118 node test system, and the optimized results that whether or not containing pumped storage units are compared and analyzed, the applicability of the proposed model and algorithm to solving large-scale multi-objective unit commitment is verified. It could effectively improve economic benefit and environmental protection of 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.

One of the most basic tasks for ship power system design is electrical load analysis, which is used to determine the required installed capacity of on-board power generators. Traditionally, the analysis is carried out based on considerations of investment cost and supply adequacy, while using a design of generators with the same rating. It does not consider the energy efficiency, thereby in some cases resulting in the generators operated at a lower efficiency under heavy loading conditions and high energy consumption. To enhance power generation efficiency and reduce energy cost, a new load analysis method for marine vessels is proposed in this paper. The relationship of operation efficiency and load factor of generators is mathematically modeled and considered in the analysis. The problem is formulated as a non-differential combinational optimization problem, where the total generation cost during a whole voyage is minimized subject to system operation and technical constraints, in order to derive the optimal generators rated power and unit commitment using genetic algorithms. The results confirmed the validity of the proposed method through load analysis of a practical ship power system, as well as energy-saving benefits using the proper generator rated power and unit commitment operation and an accurate load model for power pumps.

Guest Editorial: Situational awareness of integrated energy systems

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

This paper presents a hybrid algorithm which integrates Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving thermal unit commitment. The UC problem consists of two sub-problems: Unit Scheduled problem which is solved by PSO for minimization of transition cost and Economic Dispatch that can be solved by GA by the means of minimizing the production cost. The proposed algorithm is demonstrated for a system including ten thermal units. Running PSO and GA simultaneously justifies the production cost reduction in 24 hour period. Choosing varying PSO acceleration coefficients and inertia weight make the system convergence faster and skip the local optimums.

Unit commitment (UC) is one of the most important optimization tasks of the daily generation scheduling in power system. However, getting the global optimal solution to meet all the constraints is a comparatively tough problem. The binary coding and stochastic operators in traditional genetic algorithm (GA) are not suitable for solving large-scale UC problem. In this paper, the hybrid intelligent messy genetic algorithm (HIMGA) is proposed to solve the problem due to the characteristics of the daily generation scheduling in power system. The proposed algorithm, using operation status of unit as the genotype and combining heuristic self-adapted intelligent operators, is not only very simple but it also reduces the scale of the UC problems, improves the diversity of evolution population, enhances the searching efficiency and develops the convergence of the algorithm. The simulation results prove the correctness and validity of the proposed method.

This paper presents an optimization-based method to model the mid-term operation of a hydrothermal power system in a competitive market. Two market structures are examined: perfect competition and oligopoly comprising a dominant firm with a competitive fringe. In the latter, the dominant firm bidding strategy is determined by solving a profit maximization problem; forward contracts are also modeled. Producers submit offers to a day-ahead market, which is cleared by the system operator on a bid-cost minimization basis. A yearly planning horizon with hourly time intervals is adopted. A real size hydrothermal system, similar to the Greek Power System, is used for our tests. Hourly thermal unit commitment and start-up / shut-down decisions, market clearing prices, hydroplant generation / pumping and reservoir volumes, monthly price duration curves, ISO payment and thermal producer profits are among the results obtained.