Daily Generation Scheduling Research Articles

Optimal energy management of MG for cost-effective operations and battery scheduling using BWO

Optimal energy management of MG for cost-effective operations and battery scheduling using BWO

A model coupling current non-adjustable, coming adjustable and remaining stages for daily generation scheduling of a wind-solar-hydro complementary system

To formulate the daily generation scheduling of a wind-solar-hydro complementary system (WSHCS), the hourly forecasts of the reservoir inflow, wind speed, and sunlight intensity within a day and their longer prediction can be used to assess the benefit of the day-ahead and remaining stages, respectively. Thus, the current two-stage model coupling these forecasts can efficiently maximize the total benefits of day-ahead and remaining stages. However, the two-stage model does not consider the significant difference between non-adjustable and adjustable periods for hydropower. Namely, the hydro unit commitment should be decided in the current stage (non-adjustable period), and it can be adjusted in the coming stage (adjustable period). To address this issue, a three-stage model, including the day-ahead non-adjustable stage, the day-ahead adjustable stage, and the remaining period stage, is proposed to formulate a daily generation scheduling of the WSHCS. The Guandi wind-solar-hydro hybrid power plant on China's Yalong River is selected as a case study. Results show that compared with the two-stage model, the proposed three-stage operation model can increase the average energy production of the WSHCS by 2.23 GWh for typical days, and the average power curtailment rate reduces by 0.26%. A three-layer nested approach can simplify from 576 to 26 decision variables for the high-dimensional three-stage model, and improve the computational efficiency. Thus, the proposed method could guide the daily generation scheduling of a WSHCS. • A model coupling current non-adjustable, coming adjustable and remaining stages is proposed. • A benefit-risk indicators system considering three-stage is established. • A three-layer nested approach is used to solve the high-dimension model. • The proposed model provides guidance for daily generation scheduling.

Multi-objective generation scheduling towards grid-connected hydro–solar–wind power system based the coordination of economy, management, society, environment: A case study from China

The rapid development of solar and wind power, with their inherent uncertainties and intermittency , pose huge challenges to system stability. In this paper, a grid-connected hybrid power system that fully utilizes the complementarity characteristics in hydro, solar and wind power sources is proposed, which is capable of realizing an economic, managerial, social and environmental equilibrium in daily generation scheduling. A multi-objective optimization model is then proposed to determine the best possible solutions under nine typical scheduling scenarios, with ɛ -constraint method and fuzzy satisfying approach applied. A real case from the large-scale demonstration project in Qinghai, China clearly proved that the grid-connected hydro–solar–wind power system can provide significant comprehensive benefits. Specifically, on sunny days of normal season, the highest economic benefits can reach 1205820 × 1 0 4 CNY , consumer surplus is up to 6760 × 1 0 4 CNY , and the carbon emissions are as low as 159 × 1 0 3 tonnes . Meanwhile, hydropower, preferred as a flexible renewable power source, can effectively conduct the peak regulation, making the residual loads deviation lowest of 740.447 MWh. Future studies can focus on the long-term perspectives, further analyze the storage technology, demand response and carbon taxes. • A grid-connected hydro–wind–power generation system fully considers the generation complementarity between the different power sources. • The multi-objective scheduling optimization is developed implementing the economic-managerial-social-environmental trade-offs. • Nine typical scheduling scenarios with trapezoidal fuzzy numbers are established to handle renewables uncertainties. • Reliability analyses for each power source under different scenarios are conducted.

Multi-plan formulation of hydropower generation considering uncertainty of wind power

Abstract Being driven by wind, which has the characteristics of intermittency, the output power of a wind farm is fluctuating and difficult to forecast accurately, which imposes higher requirements on the schedule of the power system. In this paper, the fluctuation characteristics of wind power during the adjacent time interval is accurately captured through statistical analysis of the historical data of the wind farm. Then, a two-layer nested model for a hydro-wind complementary system is formulated to improve the guidance for the daily generation scheduling. In the outer layer, load distribution schemes for each station are determined using the heuristic successive cutting load method. In the inner layer, the water consumption tolerance is introduced so that the original inner model is modified into a multi-plan model of in-station economic operation, which is solved with the multi-plan solution of the dynamic programming method. A cascade hydropower station located in Qing River of China is chosen for a detailed case study. The operational results indicate the following: (1) In hydro-wind complementary system, the water consumption tolerance of a hydropower station based on the fluctuation distribution of wind power is adaptable to wind power uncertainty; (2) Using the multi-plan solution of the dynamic programming method, the optimal scheduling of the load distribution of a hydropower station based on water consumption tolerance provides a larger decision-making space for economic operation of the hydropower station; (3) The adjustable range of unit is related to the range of water head, output, and vibration zone of the hydropower station. A small change in water consumption can bring about a large change in the adjustable range; (4) The daily generation scheduling considering the adjustable range of unit can reserve the adjustment margin of the unit before the implementation of the plan, reduce the number of times the unit passes through the vibration zone during actual operation, and enhance the stability of the unit and the applicability of the daily generation plan, so as to be more adaptable to the uncertainty of wind power and decrease the impact of the uncertainty of wind power on the grid.

Scheduling and value of pumped storage hydropower plant in Iran power grid based on fuel-saving in thermal units

Abstract One of the most considerable current discussions in many countries is the valuing of pumped storage hydropower plants (PSHPs) in power grids. Calculating the value of PSHP can provide an appropriate perspective in the pricing of these large-scale storage systems. In this paper, first, a method to determine the scheduling of the Siahbishe PSHP to have the maximum impact on peak shaving and valley filling, considering the daily generation scheduling program of the thermal units in Iran power grid is proposed. Then, a mechanism for estimating the value of the PSHP in Iran’s national grid based on modified efficiency indices of the thermal units and the daily equivalent fuel-saving concept is suggested. Other benefits of the PSHP such as providing ancillary services in the power grid are not taken into account in the valuing. Therefore, the actual value of the PSHP is expected to be higher than the estimated value. To demonstrate the effectiveness of the proposed mechanism, numerical studies are done based on the actual data from Iran power grid in a two-year period (April 2015-March 2017). According to the results, the Siahbishe PSHP plays an important role in reducing production costs with an approximate annual value of 93,793,000 $.

Optimal daily generation scheduling of large hydro–photovoltaic hybrid power plants

Abstract Joint operation of large-scale renewable energy sources (e.g., hydro and solar) has become a trend in modern power systems, and more operators of existing hydropower reservoir systems are adopting it. This study aimed to improve guidance for the daily generation scheduling of a large hydro–photovoltaic (PV) power plant. First, a robust optimization model that accounts for uncertain PV power generation was formulated, in which the delivered power output of the hybrid system and hydro unit status are robust decision variables. To deal with the complexity of the model solution, a three-layer nested framework was proposed to solve the generation scheduling problem in a hierarchical structure. In the outer layer, a direct search algorithm optimizes the delivered power output of the system, aiming to maximize energy production while satisfying specified load characteristics. In the middle and inner layers, a cuckoo search algorithm and dynamic programming technique optimize the hydro unit status and load dispatch strategies, respectively, with the objective of minimizing water consumption. Finally, a decision interval on delivered power output of the system was derived by relaxing the load characteristic constraint. Results for a case study using China’s Longyangxia hydro–PV power plant indicated that the robust optimization model and the three-layer nested approach could provide effective power generation plans for the hybrid system within a reasonable time. Compared with actual operation, the power generation plans could increase energy production of the hybrid system by 1.9% while decreasing total online time of the hydro units by 9.7%. Therefore, the proposed method could improve guidance for the hydro–PV power plant’s generation scheduling. In practice, conjunctive use of the power generation plan and the decision interval could also inform flexible decision-making for plant operations management.

Daily Generation Scheduling of Cascade Hydro Plants Considering Peak Shaving Constraints

AbstractThis paper considers the operation of hydro plants, tackles the problem of daily hydro-generation scheduling (DHGS), and obtains the optimal hourly operation of cascade hydropower reservoirs. To meet the practical operational demands of power grid, an improved benefit-maximization model in which the peak shaving demands are taken as constraints is proposed for DHGS, and the joint peak load regulation (JPLR) method is applied to the proposed model. The model can enhance the power-generation efficiency and reduce the water spillage significantly under the condition of peak load regulation. Meanwhile, a hybrid method that combines discrete differential dynamic programming with progressive optimality algorithm is proposed to solve the JPLR problem. The complicated constraints can be handled by the proposed algorithm effectively. Moreover, the hydro-unit-commitment problem is solved by a rapid searching algorithm based on equal incremental principle and empirical methods. With this unit-commitment stra...

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.

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.

Three Gorges Cascade Hydropower System Joint Simulative Optimal Operation Research in Power Market

The short-term joint optimal operation simulation of Three Gorges cascade hydropower system aiming at maximum power generation benefit is proposed. And a new method for optimizing cascade hydropower station based on Adaptive Genetic Algorithm (AGA) with trigonometric selective operators is presented. In this paper, the practical optimal operation in power market is described. The temporal-spatial variation of flow between cascade hydropower stations is considered, and time of use (TOU) power price is also taken into account. Moreover, a contrast between Tangent-roulette selection operator and traditional one is made. To a certain degree, the results of simulative optimal operation based on several representative hydrographs show that Tangent-roulette wheel selection operator can find a more excellent solution, because the Tangent-roulette one can overcome the fitness requirements of non-negative. The research achievements also have an important reference for the compilation of daily generation scheduling of Three Gorges cascade hydropower system in the environment of power market.

Daily integrated generation scheduling for thermal, pumped‐storage, and cascaded hydro units and purchasing power considering network constraints

AbstractWe have developed an innovative power generation scheduling method using quadratic programming (QP). The advantage of using our method is that it simultaneously solves unit commitment and economic load dispatch. We relax the binary variables of the unit state into continuous variables to apply QP to this problem. We also add a penalty term to converge the value of those variables to 0 or 1 to the objective function: the sum of the fuel costs and the start‐up costs. This penalty term depends on the per‐unit fuel cost. The possibility of its variable converging to zero increases as the cost increases. This method was applied to a test system of daily generation scheduling that consisted of 29 thermal units, two pumped‐storage units, four cascaded hydro units, and one transmission. The schedule satisfied all constraints, that is, load‐power balance, operation reserve, power flow, minimum up/down‐times, and fuel consumption. This result shows that the proposed method is effective. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(1): 25–34, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21014

Application of enhanced PSO approach to optimal scheduling of hydro system

Abstract This paper proposes an enhanced particle swarm optimization algorithm (EPSO) to solve optimal daily hydro generation scheduling problem. In the proposed method, the improvements mainly include three aspects. Firstly, the concept of the repellor that acts complementary to the concept of the attractor is introduced into PSO, that is, the particle is made to remember its worst position also. Not only the particle’s previously visited best position but also its worst position found so far is add to the PSO to update velocity; secondly, chaotic sequences based on logistic map instead of random sequences is adopted in PSO; thirdly, a feasibility-based selection comparison technique and a heuristic rule are devised to handle constraints effectively in PSO. The feasibility and effectiveness of the proposed EPSO method is demonstrated for optimal daily generation scheduling of a hydro system and the test results are compared with those of other methods in terms of solution quality and convergence property. The simulation results show that the proposed method is able to obtain good solution.

潮流制約を考慮した火力，揚水，水力及び融通の統合翌日運用計画作成

We have developed an innovative power generation scheduling method using quadratic programming (QP). The advantage of using our method is that it simultaneously solves unit commitment and economic load dispatch. We relax the binary variables of the unit state into continuous variables to apply QP to this problem. We also add the penalty term to converge the value of those variables to 0 or 1 to the objective function; the sum of fuel costs and start-up costs. This penalty term depends on per-unit fuel cost. The possibility of its variable converging to zero increases as the cost increases. This method was applied to a test system of daily generation scheduling that consisted of 29 thermal units, two pumped-storage units, four cascaded-hydro units, and one transmission. The schedule satisfied all constraints, i.e., load-power balance, operation reserve, power flow, minimum up/down-times, and fuel consumption. This result shows that the developed method is effective.

An enhanced differential evolution algorithm for daily optimal hydro generation scheduling

The daily optimal hydro generation scheduling problem (DOHGSB) is a complicated nonlinear dynamic constrained optimization problem, which plays an important role in the economic operation of electric power systems. This paper proposes a new enhanced differential evolution algorithm to solve DOHGSB. In the proposed method, chaos theory was applied to obtain self-adaptive parameter settings in differential evolution (DE). In order to handle constraints effectively, three simple feasibility-based selection comparison techniques embedded into DE are devised to guide the process toward the feasible region of the search space. The feasibility of the proposed method is demonstrated for the daily generation scheduling of a hydro system with four interconnected cascade hydro plants, and the test results are compared with those obtained by the conjugate gradient and two-phase neural network method in terms of solution quality. The simulation results show that the proposed method is able to obtain higher quality solutions.

Application of cultural algorithm to generation scheduling of hydrothermal systems

The daily generation scheduling of hydrothermal power systems plays an important role in the operation of electric power systems for economics and security, which is a large scale dynamic non-linear constrained optimization problem. It is difficult to solve using traditional optimization methods. This paper proposes a new cultural algorithm to solve the optimal daily generation scheduling of hydrothermal power systems. The approach takes the water transport delay time between connected reservoirs into consideration and can conveniently deal with the complicated hydraulic coupling simultaneously. An example is used to verify the correctness and effectiveness of the proposed cultural algorithm, comparing with both the Lagrange method and the genetic algorithm method. The simulation results demonstrate that the proposed algorithm has rapid convergence speed and higher solution precision. Thus, an effective method is provided to solve the optimal daily generation scheduling of hydrothermal systems.

Security-Constrained Optimal Generation Scheduling in Large-Scale Power Systems

This paper presents an approach to determine the optimal daily generation scheduling based on the Spanish electricity market rules. The Spanish power system considers the pool-based daily market and the technical constraints resolution as two different and consecutive processes. After the daily market has been cleared, a centralized constraints resolution process is applied to this initial scheduling through the redispatch of previously matched energy. In this paper, an algorithm based on Benders decomposition, arranged in three levels, is proposed to solve the technical constraints solution process in order to define a preventive secure dispatch. The constraints resolution process includes a full ac network and security model. This method determines the active power committed to each generating unit so as to minimize the energy redispatch cost subject to dispatch, network, and security constraints. The solution also provides the reactive power output of the generating units, the value of the transformers taps, and the committed voltage control devices. The model has been tested in an actual example of the Spanish power system. Some relevant results are reported.

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.

Dual programming methods for large-scale thermal generation scheduling

The problem of thermal generation scheduling is considered in the framework of the short-term hydro-thermal coordination problem. Dual programming methods are applied to the large-scale problem deriving from a fine subdivision of the daily optimization horizon for networks with hundreds of thermal units. The starting point for the dual approach is obtained from the solution of a thermal scheduling problem with discarded generation ramp-rate constraints. The relaxed daily scheduling decouples into as many smaller dispatch problems as the number of subintervals. Two dual programming methods are implemented: the former is the dual active set algorithm by Goldfarb and Idnani; while the latter is based on the application of continuation method techniques. These approaches are extensively tested with reference to both a small sample system and to the daily thermal generation scheduling of the Italian (ENEL) system (over 100 thermal units and 96 quarter hour subintervals). Incorporating the dual programming approach within the ENEL hydro-thermal coordination procedure is also considered.

Daily generation scheduling: decomposition methods to solve the hydraulic problems

Short-term hydro-generation management poses a non-convex or even non-continuous optimization problem. For this reason, the problem of systematically obtaining feasible and economically satisfying solutions has not yet been completely solved. Two decomposition methods, which, as far as we know, have not been applied in this field, are here proposed : $\bullet$ the first is based on a decomposition by prediction method and the coordination is a primal-dual relaxation algorithm, $\bullet$ handling the dynamic constraints by duality, the second achieves a price decomposition by an Augmented Lagrangian technique. Numerical tests show the efficiency of these algorithms. They will enable the process in use at Electricit{\'e} de France to be improved.

Daily generation scheduling optimization with transmission constraints: a new class of algorithms

The authors describe a novel class of algorithm dealing with the daily generation scheduling (DGS) problem. These algorithms have been designed by adding artificial constraints to the original optimization problem; handling these artificial constraints by using a dual approach; using an augmented Lagrangian technique rather than a standard Lagrangian relaxation technique; and applying the auxiliary problem principle which can cope with the nonseparable terms introduced by the augmented Lagrangian. To deal with the DGS optimization problem these algorithms are shown to be more effective than classical ones. They are well suited to solve this DGS problem taking into account transmission constraints.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>