Generation Scheduling Model Research Articles

Profits Distribution Optimizing Model of Multi-Type Generation Resources Joint Scheduling

Power generation resource joint scheduling optimizing is of great significance for electric power system run economically and to achieve energy saving targets. To compare multi-types power generation resource scheduling models economic and environment benefits under different scheduling objectives and constraints, this paper took the unit output, generation resources, pollution and other aspects of constraints, respectively, took the lowest coal consumption and the smallest pollutant emissions as the target established the contract power, the energy-saving generation dispatching optimization model. Then introduced Shapley Value method, and based on it, established a generating profit distribution model. The simulation results show that energy-saving generation scheduling model can significantly reduce pollutant emissions, and Shapley Value method is better to take the profits of each generation resources into account.

Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming

This paper focuses on using DR (Demand Response) as a means to provide reserve in order to cover uncertainty in wind power forecasting in SG (Smart Grid) environment. The proposed stochastic model schedules energy and reserves provided by both of generating units and responsive loads in power systems with high penetration of wind power. This model is formulated as a two-stage stochastic programming, where first-stage is associated with electricity market, its rules and constraints and the second-stage is related to actual operation of the power system and its physical limitations in each scenario. The discrete retail customer responses to incentive-based DR programs are aggregated by DRPs (Demand Response Providers) and are submitted as a load change price and amount offer package to ISO (Independent System Operator). Also, price-based DR program behavior and random nature of wind power are modeled by price elasticity concept of the demand and normal probability distribution function, respectively. In the proposed model, DRPs can participate in energy market as well as reserve market and submit their offers to the wholesale electricity market. This approach is implemented on a modified IEEE 30-bus test system over a daily time horizon. The simulation results are analyzed in six different case studies. The cost, emission and multiobjective functions are optimized in both without and with DR cases. The multiobjective generation scheduling model is solved using augmented epsilon constraint method and the best solution can be chosen by Entropy and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods. The results indicate demand side participation in energy and reserve scheduling reduces the total operation costs and emissions.

Multi-Objective Generation Scheduling for Energy-Saving and Emission Reduction

This paper based on the idea of dynamic division of the load curve with the monotonicity of load curve and the PSO algorithm and gets the characteristics section. It fuses the sub-processes with the energy trajectory methods in order to describe the climbing peak and descending valley of the load curve closely, and characterizes the actual load curve. The multi-objective daily generation scheduling model is built based on the lowest cost of thermal power purchase of electricity and the lowest emissions of pollution gas. The optimal power flow based on combination of satisfaction degree and close degree are introduced to transform multi-objective into the single-objective optimization model. Decision makers can make interactive solution via adjusting goal satisfaction degree and close degree,so as to receive satisfactory results considering all aspects and make the daily generation scheduling based on energy-saving and environmental protection.

Promoting energy-saving and environmentally friendly generation dispatching model in China: Phase development and case studies

Energy saving and environmental protection are important conditions for the sustainable development of Chinese economy. However current widely used generation scheduling model based on predefined production quota and tariff results in heavy energy consumption and severe environment pollution. From 2007, as concerns over energy shortage and environmental pollution, the Chinese authorities introduced the implementation of energy-saving generation dispatching model, which is an important approach to facilitating energy-saving and reduction of pollutant emission. The objective of implementing energy-saving generation dispatching model is to prioritize the use of renewable energy resources and new power plants with high efficiency without compromising power system security and reliability. This paper analyzes the necessity and feasibility of implementing energy-efficient and environmentally friendly generation scheduling models in China. The institutional and technical barriers impeding the implementation of energy-saving generation dispatching model are identified. The development of advanced energy-saving generation scheduling models towards competitive market models and phased planning programs are emphasized in this paper. The effectiveness and experience of provinces piloted energy-saving generation dispatching projects are also discussed.

An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System

In this paper, a dynamic generation scheduling model is formulated, aiming at minimizing the costs of power generation and taking into account the constraints of thermal power units and spinning reserve in wind power integrated systems. A dynamic solving method blended with particle swarm optimization algorithm is proposed. In this method, the solution space of the states of unit commitment is created and will be updated when the status of unit commitment changes in a period to meet the spinning reserve demand. The thermal unit operation constrains are inspected in adjacent time intervals to ensure all the states in the solution space effective. The particle swarm algorithm is applied in the procedure to optimize the load distribution of each unit commitment state. A case study in a simulation system is finally given to verify the feasibility and effectiveness of this dynamic optimization algorithm.

A new model for optimal generation scheduling of power system considering generation units availability

The objective of the short-term combined economic–environmental dispatch is to obtain the optimal power outputs of all generating units in the system given simultaneous minimization of the corresponding fuel cost and the release of gaseous pollutants in the environment. In this paper the classical model of the dynamic combined economic–environmental power dispatch is upgraded considering the availability of the generating units present in the system. The unavailability of power generation is defined as risk index and is considered to be a function of the generating units power level. An efficient multi-objective based genetic algorithm is applied for optimization purposes. Combined hydro-thermal power system is used as a case study system. The results show increase of the availability of power generation followed by small increase of the fuel cost and the gaseous emission.

A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations

Some engineering optimization problems, such as multi-objective daily generation scheduling for the hydropower stations, the objective functions has obvious hierarchy and priority, simultaneously, the constraints is very complex. It is difficult to solve the operation problems by adopting traditional optimal technique. In this paper, a new particle swarm optimization algorithm solving hierarchy multi-objective timization problems is proposed. The algorithm can handle the level multi-objective optimization problem effectively. By adopting the adaptive inertia weight algorithm (AWA) and mutative scale local search algorithm (MSLSA), the convergence performance of the algorithm is improved. Then, A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved. Finally, Multi-objective daily generation scheduling problem of the Three Gorges cascade hydropower system during low-flow period is studied with proposed algorithm to obtain the maximum peak-energy capacity benefits, as well as power generation benefits of three gorges cascade stations.

A Stochastic Programming Model for a Day-Ahead Electricity Market With Real-Time Reserve Shortage Pricing

We present a multi-period stochastic mixed integer programming model for power generation scheduling in a day-ahead electricity market. The model considers various scenarios and integrates the idea of reserve shortage pricing in real time. Instead of including all the possible scenarios, we parsimoniously select a certain number of scenarios to limit the size of the model. As realistic size models are still intractable for exact methods, we propose a heuristic solution methodology based on scenario-rolling that is capable of finding good quality feasible solutions within reasonable computation time.

Emission-constrained optimal generation scheduling incorporating equity principles

An annual emission-constrained generation scheduling model that combines equity principles with cost optimisation is developed. By employing this annual generation scheduling model, an estimation of the daily emission allowance of each generation unit is obtained. A daily emission-constrained generation scheduling model, based on cost optimisation, is proposed. The equity-related issues in the daily scheduling model are discussed. The Shapley value is employed in the daily scheduling problem to allocate the operating cost reduction among the units. Characteristics of the allocation game of operating cost reduction in daily scheduling are discovered, and several methods are proposed to overcome the combinatorial explosion problem in the calculation of the Shapley value. The effectiveness of the proposed models is shown with simulation results on a test power system.

Short-term generation scheduling model of Fujian hydro system

The Fujian hydropower system (FHS) is one of the provincial hydropower systems with the most complicated hydraulic topology in China. This paper describes an optimization program that is required by Fujian Electric Power Company Ltd. (FEPCL) to aid the shift engineers in making decisions with the short-term hydropower scheduling such that the generation benefit can be maximal. The problem involves 27 reservoirs and is formulated as a nonlinear and discrete programming. It is a very challenging task to solve such a large-scale problem. In this paper, the Lagrangian multipliers are introduced to decompose the primal problem into a hydro subproblem and many individual plant-based subproblems, which are respectively solved by the improved simplex-like method (SLM) and the dynamic programming (DP). A numerical example is given and the derived solution is very close to the optimal one, with the distance in benefit less than 0.004%. All the data needed for the numerical example are presented in detail for further tests and studies from more experts and researchers.

Contribution to the long-term generation scheduling of the Cameroonian electricity production system

This paper presents the new long-term generation scheduling model that has been developed to improve the generation scheduling of the electric system of Cameroon. Based on the Dantzig-Wolfe Decomposition Technique (DWDT), the proposed solution approach fully exploits the particular graph structure of the hydraulic system to develop a more adapted model. The decomposition procedure of the DWDT is first applied to separate the problem formulated as a stochastic linear program into two subproblems: a hydraulic subproblem, modelled as a network flow with gains problem in a graph and a thermal subproblem. Then, an algorithm of resolution in which the subproblems are solved alternately (each by an adapted method) in a finite iterative process, is developed from the coordination procedure of the DWDT. An application of the solution approach, which can also take into account the non-linear aspects of the production system, on the electricity production system of Cameroon, permits to get in better execution time, known results.

Short-term hydrothermal generation scheduling model using a genetic algorithm

A new model to deal with the short-term generation scheduling problem for hydrothermal systems is proposed. Using genetic algorithms (GAs), the model handles simultaneously the subproblems of short-term hydrothermal coordination, unit commitment, and economic load dispatch. Considering a scheduling horizon period of a week, hourly generation schedules are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from long and mid-term models, have been used to optimize the amount of hydro energy to be used during the week. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. Results for a real system are presented and discussed.

Artificial neural networks for short-term energy forecasting: Accuracy and economic value

Sixteen electric utilities surveyed state that use of ANNs significantly reduced errors in daily electric load forecasts, while only three found otherwise. Data for five gas utilities reinforces this result: the mean absolute percentage error (MAPE) for ANN daily gas demand forecasts was 6.4%, a 1.9% improvement over previous methods. Yet ANNs were not always best, implying opportunities for further improvement. The economic value of error reduction for electric utilities was assessed by examining operating decisions. For 19 utilities surveyed, an average of $800 000/year per utility is estimated to be saved from use of ANN-based forecasts. Most benefits resulted from improved generating unit scheduling; the utilities estimated such benefits to be up to $143 annually per peak MW of demand for each 1% improvement in MAPE. This estimated worth of accuracy improvement (roughly 0.1% of annual generation O&M costs) is confirmed by solving generation scheduling and dispatch models under various levels of forecast accuracy.

Daily generation scheduling: quest for new models

This paper presents the need for adding transmission system representation in a conventional power system generation scheduling model. Given the complexity of developing solution algorithms for such models, the present study critically examines whether adding complexities to the simplified models viz., unit commitment and network flow models, is worthwhile, or the simplified models provide good approximation of the optimal generation schedule. Prototype model development and simple case studies for a capacity constrained test system shows there are major deviations in the generation schedules, load-shed requirement and spot prices obtained from the simple models and the complex model. The results indicate that research efforts towards algorithmic development for more accurate scheduling models are needed.

Framework for the incorporation of demand-side in a competitive electricity market

The paper discusses the importance of simultaneous plant production and demand reduction scheduling which is required for the establishment of a full electricity market where demand-side has opportunity to compete with generators, as is the case with the England & Wales Pool's demand-side bidding (DSB) scheme. It also emphasises that demand cannot be generally treated as negative generation because of the ability of demand to redistribute itself in response to price based load management activities. In that sense, an adequate scheduling methodology of available resources (from supply and demand-sides) is needed to facilitate the new market. However, traditional formulations of the plant scheduling problem are not valid when load reduction is available, as gross demand is not known in advance. For that purpose a composite model for optimal generation and demand reduction scheduling is presented in the paper. It is shown that this model can be used for a comprehensive evaluation of possible scenarios for the implementation of demand-side bidding into the electricity market and the assessment of the influence of DSB on total production costs, system marginal price (SMP) profile, capacity element payments and benefit allocation between producers and consumers.

A new generation scheduling program at Ontario Hydro

The new daily generation scheduling model for the Ontario Hydro system is presented. The model is capable of considering the thermal and hydraulic systems together with system reserve requirement and transmission losses. The hydraulic system is considered in a detailed formulation that can handle river couplings in a complete manner. Inability to do this was a major weakness in the peak shaving technique used since early 1970s. A special network programming technique has been used to solve the problem. This method has proved to be very efficient from a computational point of view. Test results from application of the new technique are presented and compared to the peak shaving method. The user has estimated 1.4 million dollars per year of savings from the new computer package. >

Short-Term Generation Scheduling of Hydraulic Multi-Reservoir Multi-Area Interconnected Systems

This work presents a generation scheduling model for multi-reservoir interconnected systems, to be used in operation planning as an auxiliary tool in the determination of the feasibility of weekly operation policies. The nonlinearities associated with the operation of each hydro plant are represented in detail. The hydro generation and the energy interchanges between control areas are scheduled in order to meet the weekly operation targets. The problem is solved by Dynamic Programming and the Principle of Progressive Optimality is used to allow its temporal decomposition. Sa tisfactory computational results have been obtained and the model is now being incorporated into the operation planning of the Brazilian system, which is composed by about 50 reservoirs and 8 control areas.