Long-term Generation Scheduling Research Articles

An Annual Electric Energy Trade Scheduling Model under the Dual Track Mode

The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the “market electricity” and “planned electricity” double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of “market electricity” and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, “market electricity” capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China.

An Efficient Optimization Method for Long-term Power Generation Scheduling of Hydropower Station: Improved Dynamic Programming with a Relaxation Strategy

An Efficient Optimization Method for Long-term Power Generation Scheduling of Hydropower Station: Improved Dynamic Programming with a Relaxation Strategy

Long-Term Generation Scheduling: A Tutorial on the Practical Aspects of the Problem Solution

Long-Term Generation Scheduling: A Tutorial on the Practical Aspects of the Problem Solution

Long‐term generation scheduling for renewable‐dominant systems concerning limited energy supporting capability of hydrogeneration

Renewable-dominant mixtures with hydrogeneration and variable renewable energies (VREs) have been the focus for decarbonization of power systems. Hydrogeneration with large reservoir becomes the main regulating source. The long-term generation scheduling (LTGS) on monthly reservoir regulation for the approaching year becomes more significant because reserving adequate water storage is related to intramonth balance, considering that hydrogeneration has to supplement deficit energy of VRE generation. However, conventional LTGS method that is based on monthly averages overestimates the hydrogeneration's intramonth energy supporting capability, whereas rolling short-term generation scheduling method cannot account the long-term effect of reservoir regulation decisions. This paper proposes a new LTGS method accounting intramonth imbalance risk that the energy deficit of VRE generation exceeds the maximum supporting energy of hydrogeneration. Since intramonth imbalance risk is coupled with LTGS in an uncertain and implicit way, intramonth extreme scenarios are introduced based on distribution quantiles of natural inflow of reservoirs and VRE generation, and the probabilistic intramonth imbalance risk constraints can be transformed as the hydrogeneration must supplement energy deficit of VRE generation during the extreme scenarios. A case study is conducted on a Wind-PV-Hydro energy base of southwestern China. The proposed method is found able to improve the feasibility and reliability of LTGS compared to other LTGS methods. Moreover, the high calculation efficiency of conventional LTGS method is inherited.

Inflow Aggregation and Run-of-the-River Inflow Energy for Reducing Dimensionality in the Long-Term Generation Scheduling Problem

The long-term generation scheduling (LTGS) problem aims to build an operating policy over a multi-year planning horizon, correlating thermal generation and deficit costs with water storage. The LTGS is modeled as a linear multistage stochastic program, whose state-of-art solution is the stochastic dual dynamic programming (SDDP). One critical challenge is to represent the time-dependency of river inflows accurately. Despite recent advances, modeling simplifications are needed to allow the LTGS computational tractability via SDDP since it is necessary to increase the state space to include the time-dependent variables properly. Thus, most works simplify the hydro production function (HPF) to represent the inflows in detail to overcome this negative aspect. However, the literature lacks this trade-off, i.e., finding a balance between stochastic inflow modeling and HPF representation. Thus, this paper proposes an alternative approach to analyzing this trade-off, using the inflow aggregation and run-of-the-river energy inflow instead of individual inflow in SDDP. The proposed approach drastically reduces the number of state variables in SDDP, allowing a detailed HPF representation in the LTGS. We employ a one-sided confidence interval for expected cost estimation to show that our approach provides better performance than the individualized inflows. The analysis is performed in several large-scale computational instances using a power system with 53 geographically widespread hydro plants. The numerical results demonstrate that inflow aggregation provides, on average, a 4 % reduction on the operating cost, whereas for the run-of-the-river inflow energy, the reduction is, on average, 2 %.

Two-stage and multi-stage decompositions for the medium-term hydrothermal scheduling problem: A computational comparison of solution techniques

In hydro-based power systems, the Generation Scheduling (GS) problem is usually divided into a series of coupled models aiming to obtain an optimal operating policy over a multi-year planning horizon. The medium-term GS (MTGS) problem belongs to these models, linking the long-term GS (LTGS) with the short-term GS (STGS) problem. To carry out a proper coupling, we propose a stochastic MTGS model with a multi-period scenario tree with non-common samples, and hourly discretization in the first week to include the nonconvex thermal unit-commitment constraints. These considerations lead to solving a very challenging large-scale stochastic optimization problem with mixed-binary variables in the first week. In this context, efficient solution techniques must be explored to ensure a suitable trade-off between solution accuracy and computational performance. In this paper, we propose a two-stage decomposition for the MTGS that is solved by the extended level bundle method. Every second-stage subproblem is itself a multi-period stochastic problem involving only continuous variables. Numerical assessments on the large-scale Brazilian MTGS problem indicate that our approach significantly outperforms the Nested decomposition and Stochastic Dual Dynamic Programming (SDDP) algorithm in terms of computational burden, providing CPU time reductions in the order of 19%.

Multiobjective Long-Term Generation Scheduling of Cascade Hydroelectricity System Using a Quantum-Behaved Particle Swarm Optimization Based on Decomposition

Multiobjective long-term generation scheduling (MOLTGS) plays a vital role in coordinating the contradiction between the generation and reliability of cascade hydroelectricity system (CHS). In this paper, a multiobjective quantum-behaved particle swarm optimization based on decomposition (MOQPSO/D) is presented to solve the MOLTGS problem of maximizing total hydroelectricity generation and firm hydroelectricity output. In MOQPSO/D, the improved logistic map is adopted to initialize the population in the feasible space, where various equality and inequality constraints are handled by a constraint handling method based on conversion and repair strategies. An improved Tchebycheff decomposition with a modified generator of direction vectors, a modified QPSO operator and normal cloud mutation are the key components of MOQPSO/D in addressing the nonlinearity and complexity of MOLTGS. The feasibility and effectiveness of MOQPSO/D are verified first on numerical experiments of benchmark instances and then on real engineering examples of the Three Gorges (TG) and Gezhouba (GZB) plants within three representative years. The results show that MOQPSO/D can not only perform better robustness, convergence and diversity performance than other seven competitors but also output a group of Pareto optimal schemes within a reasonable amount of time. So, this paper provides a new effective alternative to solve the MOLTGS problem of CHS when considering both generation and reliability objectives.

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

Multi-objective long-term generation scheduling (MLGS) considering ecological flow demands is important for comprehensive utilization of water resources in cascade hydropower system (CHS). A novel adaptive multi-objective particle swarm optimization based on decomposition and dominance (D2AMOPSO) is developed in this paper to solve the MLGS problem. In D2AMOPSO, a constraint handling method based on repair strategy and individualconstraints and group constraints (ICGC) technique is embedded to address various constraints. An improved logistic map is adopted to initialize the population. During the evolutionary process, an improved Tchebycheff decomposition is introduced to select personal best and global best for each particle, and the non-dominated solutions found so far are stored in an external archive where crowding distance and elitist learning strategy are performed to improve its diversity. Meanwhile, an adaptive flight parameter adjustment mechanism based on Pareto entropy is adopted to balance the global exploration and local exploitation abilities of the population. A normal cloud mutation operator is used to keep the population diversity and escape local minima. In the case study of the Three Gorges Cascade hydropower system (TGC) under three typical years, the results of the proposed method and other four competitors show that D2AMOPSO can obtain better diversity and faster convergence solutions for the MLGS problem in less time.

Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent values. The proposed model is applied to perform the long-term generation scheduling for the Wu River cascade hydropower plants and achieves an increase of 106.93 million yuan of annual income compared with the conventional scheduling model, without considering price scenarios, showing better performance in effectiveness and robustness in multiple markets.

Series division method based on PSO and FA to optimize Long-Term Hydro Generation Scheduling

The fundamental requirement of power system hydro scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Long-Term Hydro Generation Scheduling (LHGS) is a complicated nonlinear, non-convex and nonsmooth optimization problem with discontinuous solution space. The model considers daily water inflows, limits on reservoir level, power generation depends on the available head of hydro units caused by power variations, start-up, and shut-down of hydro units. To deal with this complicated problem, Series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed in this paper. The SDM is to make a division on the Swarm Intelligence (SI) algorithm which is to be a number of particles searching collections that properly can be regarded as divisions. Whereas, each division is a developmental algorithm which used to get the global point. The extent of the SDM is often offered a quicker convergence so as to accomplish the best initial operation to swarm's algorithm research. The proposed SDM are tested on two test systems actual observed system operator (AOSO) and Standard System Operation (SSO) and compared with some recent research works in the area. The results point out the Series Division Firefly Algorithm (SDFA) is robust and has good efficiency and superiority.

Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches

Abstract The long-term generation scheduling (LTGS) problem aims at finding a generation policy that minimizes an objective function over a multi-year planning horizon. A crucial aspect of this problem is the Hydropower Production Function (HPF), which relates power with head, turbined outflow, and efficiency of the generating units. Given that the LTGS is a large-scale stochastic optimization problem, the HPF is modeled in a simplified manner. However, considering the high-performance computers currently available and the recent advances in stochastic optimization algorithms, it is possible to enhance the HPF modeling to use the energy resources more efficiently. This paper proposes a piecewise linear model of HPF that considers the plant generation as a function of the volume and the total outflow. Unlike previous works, the HPF also considers the (nonlinear) efficiency function of each generating unit. The paper also presents a comparison between the proposed HPF and a one-dimensional HPF known as constant productivity. The generation policy and the computational burden are analyzed using an optimization-simulation process based on Stochastic Dual Dynamic Programming algorithm. The computational tests use data of a large-scale electrical power system, which corresponds to about 90% of the Brazilian system.

Runoff forecasting benefit evaluation for long-term power generation scheduling

Long-term runoff forecasting important reference significance for the long-term planning of cascade hydropower stations. The traditional forecast accuracy evaluation is based on the deviation between the predicted runoff and the measured hydrological sequence, but fails to consider the effect on long-term scheduling. In this paper, a runoff forecasting evaluation method for long-term scheduling is presented. First, a monthly distribution method based on the forecast value of annual runoff is proposed to describe the uncertainty of the forecast. Then, a power generation plan model with the maximum generation objective and an actual generation benefit evaluation model are established to study the effect of runoff forecasting in scheduling. At last two indexes of “Incremental generation” and “Incremental benefit” based on the comparison of actual benefit with and without a forecast plan are given to evaluate the performance of forecasting. The case study shows that the proposed evaluation method can reflect the actual benefit brought by the forecast information, which provide more practical guidance for the hydropower station.

A random gradient based Harmony search algorithm for long-term hydropower scheduling in the lower of Jinsha River Basin

The long-term hydropower generation scheduling (LHGS) is one of the most important problems which aim to obtain a suitable scheme to maximize electrical benefits. In this paper, a gradient based Harmony search algorithm (GHS) is employed on the optimization of the cascade hydropower stations in the Jinsha River. Firstly, the original Harmony search algorithm (HS) is firstly improved by a dynamically adjustment strategy and global pitch adjustment strategy. Then random gradient strategy is adopted to improve the search speed of HS. In case study, the proposed GHS is used to solve the LHGS problem in the Jinsha River. Results show that GHS is effectiveness when dealing with LHGS problems.

Impact of large-scale rooftop solar PV integration: An algorithm for hydrothermal-solar scheduling (HTSS)

Hydrothermal scheduling is a standard problem in the operation of power systems. With the integration of renewable resources, like solar and/or wind to the existing hydro-thermal system, there is a need for a dedicated approach for long-term generation scheduling of the integrated system. Long-term generation scheduling of hydro-thermal-solar systems at the grid level has not been reported so far. This paper discusses a Hydrothermal-Solar Scheduling (HTSS) algorithm for the same. The algorithm is a two-stage formulation using dynamic programming and linear programming techniques. This generic methodology can be used for assessing the impact of large-scale rooftop solar-PV integration with a hydrothermal system, in terms of the electricity energy-mix, generation schedules and the annual generation-costs of the concerned system. It has been illustrated by application to the problem of integration of a large-scale rooftop solar photovoltaic scenario with the existing Mumbai sub-system.The results show a significantly higher annual generation-cost saving, achieved by integrating the large-scale rooftop solar photovoltaic scenario via the proposed HTSS algorithm. The benefits, however, reduce with increasing penetrations of solar in the electricity energy-mix. The annual generation-cost savings have also been used for estimating the upper cap on feed-in tariffs for solar generation, and the financial viability indicators for the large-scale rooftop solar photovoltaic scenario; the corresponding internal rate of return is found to decline from 18.4% to 16.6% as the share of solar-PV in the electricity energy-mix of Mumbai increases from 2.8% to 13.9%.

Long-Term Generation Scheduling of Hydropower System Using Multi-Core Parallelization of Particle Swarm Optimization

A multi-core parallel Particle Swarm Optimization (MPPSO) algorithm is developed to improve computational efficiency for long-term optimal hydropower system operation, in response to rapidly increasing size and complexity of hydropower systems, especially in China. The MPPSO can be implemented in three steps with easily accessible multi-core hardware platforms. First, a multi-group parallel computing strategy is introduced to maintain the diversity of population for finding the global optima. Second, the fork/join framework based on divide-and-conquer strategy is adopted to distribute multiple populations to different CPU cores for parallel calculations to take full advantage of CPU performance. Third, the results generated in different CPUs are merged to achieve an improved acceleration effect on computational time cost and more accurate optimal scheduling solution. Results for a system of twelve hydropower stations in the Guizhou Power Grid in China demonstrate that the proposed algorithm makes full use of multi-core resources, and significantly improves the computational efficiency and accuracy of the optimal solution, in addition to its low parallelization cost and low implementation cost. These suggest that the proposed algorithm has great potential for future optimal operation of hydropower systems.

Optimal Long-Term Hydro Generation Scheduling of Small Hydropower Plant (SHP) using Metaheuristic Algorithm in Himreen Lake Dam

This study focuses on the improvement of optimization model by applying particle swarm optimization and firefly algorithm methods to get a stable power production utility at its maximum level. Furthermore, it investigates on the minimization of utility loss in power production from the hydropower system, which is done by optimizing the variables of operation control in the hydropower plant at Lake Himreen - Diyala Dam. The variables mentioned are net turbine head, the rate of water flow and power production which had been gathered in the data during a research throughout a 10-year period. The results obtained from these two methods, namely Firefly Algorithm (FA) and Particle Swarm Optimization, (PSO) are compared. The inferences for general comparisons are created through several behavior indicators. The behavior indicators illustrate that FA's performance is better than PSO's performance, in some fields. At the end, the results show the strength of FA, as well as its efficiency and superiority.

多目标水火协调长期优化调度模型研究

针对水火电力系统长期优化调度问题，将电网最优潮流和水火电站长期优化调度相结合，提出了考虑水电发电量最大、水电弃水量最少、火电煤耗最低以及全网网损最小的多目标水火电联合优化调度模型。该模型考虑了水电站水位约束、水电站流量约束、水量平衡约束、水火电源出力约束、潮流计算功率平衡约束以及电网安全约束，采用线性加权方法将多目标转化为单目标。以每月典型日负荷曲线为基础，将水电逐次切负荷与火电等微增率分配机组出力相结合，计算水火电出力分配、电网潮流及网损，最后采用遗传算法进行求解。通过IEEE-30节点系统模拟调度表明，本文所提方法能够有效减少水电弃水并显著降低系统总损耗，提高了电力系统的经济性，是一种可行的水火电长期联合优化调度方法。 Focus on the problem of long-term generation scheduling of hydrothermal system, this paper combines OPF and long-term optimal scheduling hydrothermal system that proposes the multi-objective model of generation maximization of hydropower, spillage minimization of hydropower, consumption minimization of coal-fired thermal power and line loss minimization. The model takes into account the hydraulic constraints, water balance constraints, security constraints in the power grid, power balance constraints and power supply. The multi-objective is transformed into a single objective by using the linear weighted sum method. On the basis of the typical daily load curve, hydrothermal power output distribution and the power flow and network loss are calculated with hydropower successive cutting load and thermal power equal incremental rate. Finally, the genetic algorithm is used to solve this problem. IEEE-30 bus system is used to verify the effectiveness of the proposed method. The numerical results show that the generation scheduling obtained from the proposed method can reduce the total loss of the power grid; it’s a feasible long-term optimal dispatching method.

Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error

Reliable streamflow forecasts are very significant for reservoir operation and hydropower generation. But for monthly streamflow forecasting, the forecasting result is unreliable and it is hard to be utilized, although it has a certain reference value for long-term hydro generation scheduling. Current researches mainly focus on deterministic scheduling, and few of them consider the uncertainties. So this paper considers the forecasting error which exists in monthly streamflow forecasting and proposes a new long-term hydro generation scheduling method called forecasting dispatching chart for Xiluodu and Xiangjiaba cascade hydro plants. First, in order to consider the uncertainties of inflow, Monte Carlo simulation is employed to generate streamflow data according to the forecasting value and error distribution curves. Then the large amount of data obtained by Monte Carlo simulation is used as inputs for long-term hydro generation scheduling model. Because of the large amount of streamflow data, the computation speed of conventional algorithm cannot meet the demand. So an improved parallel progressive optimality algorithm is proposed to solve the long-term hydro generation scheduling problem and a series of solutions are obtained. These solutions constitute an interval set, unlike the unique solution in the traditional deterministic long-term hydro generation scheduling. At last, the confidence intervals of the solutions are calculated and forecasting dispatching chart is proposed as a new method for long-term hydro generation scheduling. A set of rules are proposed corresponding to forecasting dispatching chart. The chart is tested for practical operations and achieves good performance.

Long-Term Generation Scheduling Method with Constrained Numbers of Unit Commitment and Fuel Constraints on Thermal Units

SUMMARY The long-term generation scheduling in power utilities is aimed at maintaining power supply sufficiency and estimating fuel consumption. Multiperiod constraints, such as the allowable number of unit commitments of thermal units and fuel consumption with a given total amount of fuel, must be taken into appropriate consideration for practical economic scheduling in the long-term scheduling horizon. Due to the large size and complexity of such scheduling problems, it is difficult to obtain solutions collectively in terms of stability and processing time in operational situations. We propose a new scheduling method which consists of “preparative processing” and “detailed optimization processing.” The former acts to divide the long-term problem with multiperiod constraints into time units of a week according to quick and simplified scheduling results, and the latter acts to minimize the total operation cost on the basis of those weekly partitioned problems. The processing starts with an optimal scheduling result excluding the aforementioned two multi-period constraints and proceeds to sequentially resolve the violation of each constraint with quantitative consideration of interrelationships between them. This paper describes the validation and effectiveness of the proposed method through the real world example of Chubu Electric Power Company.

Interior point method for long-term generation scheduling of large-scale hydrothermal systems

This paper presents an interior point method for the long-term generation scheduling of large-scale hydrothermal systems. The problem is formulated as a nonlinear programming one due to the nonlinear representation of hydropower production and thermal fuel cost functions. Sparsity exploitation techniques and an heuristic procedure for computing the interior point method search directions have been developed. Numerical tests in case studies with systems of different dimensions and inflow scenarios have been carried out in order to evaluate the proposed method. Three systems were tested, with the largest being the Brazilian hydropower system with 74 hydro plants distributed in several cascades. Results show that the proposed method is an efficient and robust tool for solving the long-term generation scheduling problem.