Short-term Optimal Operation Research Articles

Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

Risk Control in Optimization of Cascade Hydropower: Considering Water Abandonment Risk Probability

Water abandonment risk caused by inflow uncertainties is a major problem in cascade hydropower generation operation and water resources management. In this study, we propose a theoretical estimation method (TEM) to calculate water abandonment risk probability (WARP) utilizing sample inflow data and historical forecast errors. The method analyzes the differences in risk quantification between the head hydropower station and downstream stations, and the corresponding probability formula is provided. Then, a short-term optimal operation model of cascade hydropower stations considering the additional WARP water level constraints is constructed to dynamically control water level risk. The Dahuashui (DHS)-Geliqiao (GLQ) cascade hydropower stations in the Wujiang River Basin of China is investigated as a case study. The results show that compared with the historical scheme, the total amount of water abandonment of DHS, GLQ and DHS-GLQ in the WARP optimal scheme decreases by 11.69%, 47.69% and 28.27%, respectively, and the flood peak of GLQ is reduced by 21.6%. In conclusion, compared with the traditional control approach and actual operation processes, the proposed risk control in optimization of cascade hydropower considering WARP can improve the comprehensive utilization efficiency of cascade hydropower systems by reducing the occurrence of water abandonment and thereby increase generation profits.

Identifying effective operating rules for large hydro–solar–wind hybrid systems based on an implicit stochastic optimization framework

Optimization of operational strategies of a hydro–solar–wind hybrid renewable energy system (HRES) could increase resource use efficiency without additional investment. However, because of the myopic nature of the short-term forecast information, the short-term optimal operation strategy of the large HRES cannot guarantee a satisfactory long-term system performance. Therefore, it is necessary to explore reasonable long-term operation strategies for the hydro–solar–wind HRES, especially the establishment of operating rules. In this study, we proposed a method to identify effective long-term operating rules for the large hydro–solar–wind HRES based on an implicit stochastic optimization framework. A large hydro–solar–wind HRES located in the upper Yellow River, China was selected as a case study. It was found that: (1) Operating rules taking reservoir storage or water level as the output variable and available energy as the input variable were most effective to guide the complementary operation of the hydro–solar–wind HRES; (2) Compared with a standard operating policy, the identified best operating rules could increase energy production and guaranteed rate of the HRES by 5.2% and 4%, respectively; and (3) Complementary management of hydro, solar and wind could increase the energy production of the hydropower system, when power transmission capacity was large enough.

Considering water propagation impact in short-term optimal operation of cascade reservoirs using Nested Progressive Optimality Algorithm

Difficulty remains to represent water propagation impact to construct water balance constraints for the Short-Term Cascade Reservoirs Optimal Operation (SCROO), due to the complex water propagation mechanism existing between cascade reservoirs. The imprecise representation of water propagation impact hinders the implementation of hydropower generation schemes and leads to operational risks. Thus, a modified SCROO model considering water propagation impact was proposed in this study to improve the accuracy of cascade operation. Specifically, water propagation is expressed using the Muskingum method in the modified SCROO model, and characterized by two variables, including propagation time related to the magnitude of the upstream reservoir’s outflow and flow attenuation related to the propagation time, which were identified using the successive approximation method under water balance constraints within the modified SCROO model. Subsequently, this research applied the Nested Progressive Optimality Algorithm (NPOA) in the model calculation innovatively, in which the stage benefit function and nest computation could be improved. The modified model and algorithm developed herein were applied and validated on cascade reservoirs along the Yalung River in China by comparing it with traditional methods and algorithms. The results show that: 1) Compared to traditional methods, considering water propagation within water balance constraints as a dynamic parameter can calculate the inflow of downstream reservoir more accurately and mitigate risks in the actual operation of cascade reservoirs. Besides, the reservoir inflow calculated by traditional methods will lead to different magnitudes of benefit loss; and 2) the NPOA can significantly reduce dimensionality problems caused by the water propagation impact and greatly improve the actual hydropower generation than that of a traditional optimal algorithm, Multi-Stage Dynamic Programming (MSDP). Compared to the MSDP, the NOPA is more applicable in a cascade system with a larger number of reservoirs. The modified model and algorithm can significantly improve the effectiveness of cascade reservoir optimal operation schemes and provide more information for decision-makers.

Consideration of streamflow forecast uncertainty in the development of short-term hydropower station optimal operation schemes: A novel approach based on mean-variance theory

The uncertainty of streamflow forecast will cause the ineffectiveness on the short-term hydropower station optimal operation (SHSOO) and lead to the risk of producing less hydropower than planned. Traditionally, stochastic optimal theory represented by the stochastic dynamic programming model (SDPM) is commonly used for SHSOO, which is focus on the uncertainty during a single lead time and lack of an overall consideration of multiple independent lead times of differing length. Thus, this paper proposes a short-term hydropower station optimal operation model based on mean-variance theory (MV-SHSOOM) for reducing operational risk. The proposed multi-step model considers the forecasting uncertainty of different lead times as an integral component to guide a station’s optimal operation. Specifically, the forecast uncertainty was integrated by the joint probability distribution of forecast error using a multivariate hydrologic uncertainty processor (HUP) at first. Then, possible observed streamflow sequences were obtained using the joint probability distribution of forecast error and taken as input to the short-term optimal operation model of the hydropower station. Finally, the mean-variance theory was applied to construct the optimal operation model for balancing the power generation and operational risk. The proposed model was applied to the Jinxi hydropower station, China, to determine its performance. The results show that: 1) the dependent structure among the forecast errors at different lead times is significant in terms of Spearman correlation, and the joint probability distribution of forecast error can be effectively described by a multivariate HUP; 2) the proposed MV-SHSOOM is superior to the traditional deterministic dynamic programming model (DDPM) and SDPM, and can more effectively reduce operational risk while increasing power generation.

Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems

Formulating an optimal operation strategy is a real challenge in the actual operation stage of a hybrid system. This study presented a research framework for determining the short-term optimal operation strategy for cascade hydro-photovoltaic hybrid system. First, a method was presented for describing the cascade hydro-photovoltaic hybrid system net load utilizing the daily average net load and fluctuation coefficient. Second, a model was established for the short-term optimal operation of the cascade hydro-photovoltaic hybrid system. Then, a synchronous peak-shaving strategy of cascade hydropower stations was proposed based on the fluctuation coefficient. Finally, the framework was applied to a case study in the Qinghai Province, China. The results showed an obvious competitive relationship between the two objectives. In the hybrid system, the peak-shaving task is mainly carried out by a daily regulation hydropower station, while its base-load task is jointly carried out by runoff and daily regulation hydropower stations. When the average daily runoff is less than 30% of the hydropower rated flow, the cascade hydro-photovoltaic hybrid system should bear single-peak load on sunny, cloudy, and dusty days, otherwise, it should bear double-peak load. It should bear double-peak load under different daily average runoffs on rainy and snowy days. After the cascade hydropower relative position was interchanged, the cascade hydropower overall peak-shaving capability decreased. Thus, the above results prove the feasibility of the research framework, and it provides a reference for the short-term operation of cascade hydro-photovoltaic hybrid system.

Hybrid Time-Scale Optimal Scheduling Considering Multi-Energy Complementary Characteristic

Evaluating the potential utilization of hybrid energy systems and determining the multi-scale optimal operation strategy is critical to power system planning in the context of energy structure adjustment, especially for large-scale hybrid energy systems. Considering the long-term and short-term complementary characteristics, this paper puts forward a coordinated optimization framework for the integrated energy system in the world's largest multi-energy complementary base on Yellow River's upper reaches. The main procedures are as follows: 1) cross-correlation method is introduced for individually analyzing the long- and short-term complementary characteristics of wind power, photovoltaic, and hydropower in this multi-energy complementary base; 2) a double-layer model combining the long-term optimal operation model and short-term optimal operation model for determining the proportion of multiple energy and optimizing the maximum peak-shaving ability; 3) Large-Scale System Decomposition-Coordination Method is applied for solving the proposed double-layer operation model. The results show that wind power 23%, photovoltaic 35%, hydropower 42% can keep the most stable generation in the long-term complementary operation. This proportion results can improve the system peak regulation capacity with 50.8% (sunny day's morning peak) and 24.2% (rainy day's morning peak) in the optimal short-term operation.

Optimal solutions for fixed head short-term hydrothermal system scheduling problem

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems.

Robust online scheduling for optimal short-term operation of cascaded hydropower systems under uncertainty

The uncertainties in system and model parameters arising from the volatility of market, weather and operating conditions pose a major challenge to the optimal short-term operation of cascaded hydropower systems. The dynamic operating environment resulting from the fluctuating parameters greatly impacts the scheduling of power generation and generating unit commitment in such systems. This article focuses on the development and implementation a novel rolling horizon robust online scheduling framework that utilizes stochastic optimization within a model-based feedback scheme to tackle the uncertainties in electricity prices, electric power demands, water inflows and plant model parameters. The efficacy of this approach is demonstrated through application to a variety of case studies for different types of uncertainty. Case studies demonstrate significant improvements in system performance with the proposed strategy, in terms of system economics and constraint satisfaction, over schedules generated without feedback or use of a nominal online scheduling scheme.

Short-Term Optimal Operation of Baluchaung II Hydropower Plant in Myanmar

The short-term optimal operation model discussed in this paper uses the 2016 to 2018 daily and monthly data of Baluchaung II hydropower station to optimize power generation by minimizing water consumption effectively in order to get more revenue from optimal operation. In the first stage, run-off-river type Baluchaung II hydropower station data was applied in a mathematical model of equal micro-increment rate method for optimal hydropower generation flow distribution unit results. In the second stage, dynamic programming was used to get optimal hydropower generation unit distribution results. The resultant data indicated that optimized results can effectively guide the actual operation run of this power station. The purpose of the optimal load dispatching unit was to consider the optimal power of each unit for financial profit and numerical programming on the actual data of Baluchaung II hydropower plant to confirm that our methods are able to find good optimal solutions which satisfy the objective values of 17.75% in flow distribution units and 24.16% in load distribution units.

Optimal short-term operation of pumped-storage power plants with differential evolution algorithm

In this study, the optimal short-term operation of the lossy electric power system which included the pumped-storage power generation unit (Ps) was carried out. The problem is quite complex in terms of its constraints and being a mathematically difficult problem. Therefore, in order to obtain optimal solutions of the problem, differential evolution algorithm (DE) was used. In this study, one day operation of a system, which includes thermal and Ps units, solved with different optimization algorithms in the literature was addressed. The addressed system was solved in two different ways. First of all, the solution is made assuming that the system consists only of thermal generation units. Then, a Ps unit was added to a bus in the system and it was solved again and the benefit obtained was discussed. In the solution of the sample system, transmission line losses are taken into consideration and Newton-Raphson power flow method was used to calculate these losses. The sample system was solved 30 times with DE and the optimal solution values were determined. All possible constraints in problem solving were provided through the punishment method. The results obtained from the solutions of the sample systems were compared and discussed.

Short-term optimal operation of wind-solar-hydro hybrid system considering uncertainties

Due to the deterioration of non-renewable energy resources, the operation of wind-solar-hydro hybrid systems has become a prominent research topic. However, owing to the uncertainties in such a hybrid system, such as those in wind speed, solar radiation intensity, and power load, it is difficult for a dispatcher to develop the power generation plan of the day following the planning day (referred hereafter as the next day). The purpose of this study is to consider the uncertainties in a short-term optimal operation model and obtain for each state variable, the probability density function of the operation process of the next day. The latter can provide a dispatcher with a large amount of reliable decision reference information. In this study, simulation-estimation method is proposed to characterize the uncertainty and estimate the probability density function of the operation process. First, three short-term optimal operation models are established to provide flexible options to dispatchers. Second, a stochastic simulation method based on probabilistic forecasting is proposed to generate simulation scenarios for the next day. Third, each simulation scenario is input into the constructed optimal operation model for obtaining the solution. Fourth, the kernel density estimation method is used to estimate the probability density function of the operation process of the next day. Finally, the constructed optimal operation model and proposed simulation-estimation method are applied to the wind-solar-hydro Experimental Base of the Yalong river basin in China. In this case, the differences between the three operation models in three typical seasons are compared. Based on the verification of the prediction mean scenario and observation scenario, the experimental results show that the proposed model and method of this study are practical and effective.

Uncertainty management via online scheduling for optimal short-term operation of cascaded hydropower systems

The efficient utilization of available resources plays a significant role in the operation of cascaded hydropower systems. Scheduling of such systems may be used to determine power generation and generating unit decisions that optimize economic performance subject to prevailing constraints. The highly volatile and unpredictable nature of market and weather conditions give rise to uncertainties in electricity prices and water inflows, which may significantly impact the power generation schedules for these systems. Computed schedules may also give rise to significant variation in the generating unit commitments in response to slight fluctuations in electricity pricing, a phenomenon referred to as “nervousness”. This article focuses on the development and implementation of a novel rolling horizon online scheduling scheme capable of handling uncertainties in key system and model parameters. Different strategies for mitigating the undesirable effects of nervousness are also investigated. These approaches are demonstrated through application to several case studies.

Short-Term Optimal Operation of Cascade Reservoirs Considering Dynamic Water Flow Hysteresis

In making short-term optimal operation schemes of cascade reservoirs, water flow hysteresis between the upper reservoir and the lower reservoir is often considered as constant, which cannot really reflect the hysteresis variation between different water flows and will lead to losses of the optimal operation scheme’s benefit. To depict the water flow hysteresis, a Dynamic Water Flow Hysteresis Method (DWFHM) is proposed, based on the space mapping principle. With the mapping operator in the DWFHM, the lower reservoir inflow can be directly obtained. Besides, the DWFHM is used to deal with the hydraulic relation constraint in the short-term optimal operation model of cascade reservoirs. Then, the improved model is applied to the Jinguan cascade reservoirs in the Yalong River basin and solved by an Improved Progressive Optimal Algorithm (IPOA). The results are as follows. (1) Compared with the traditional Fixed Water Flow Hysteresis Method (FWFHM), the inflow processes of the lower reservoir obtained by the DWFHM are more in line with the actual values, due to full consideration of the attenuation effect. (2) The optimal operation with the DWFHM can effectively increase the generated energy (2827 MW·h and 504 MW·h in the non-flood season and the flood season, respectively). Through the analysis of this case, the DWFHM developed in this study can effectively improve the practicability of the optimal operation scheme and reduce the risk in the operation of cascade reservoirs.

Optimal Short-Term Operation of a Cascaded Hydro-Solar Hybrid System: A Case Study in Kenya

In this paper, we propose an optimal dispatch scheme for a cascaded hybrid hydro-solar power system, i.e., a hydroelectric system coupled with solar generation, which maximizes the head levels of each dam, and minimizes the spillage effects. As a result, more water is stored in the dams to meet a given amount of energy providing more flexibility to the system in dry months. This dispatch scheme is based on the development of a simplified hydroelectric power system model, which has low computational burden and may be implemented for the short-term operation of a cascaded hydro-solar hybrid power system. To this end, the non-convex relationships that describe the system physical constraints, e.g., hydroelectric power output, are transformed into affine relationships; thus, reducing the computational complexity. The transformations are based on the construction of convex envelopes around bilinear functions, piecewise affine functions, and exploitation of optimization properties. We validate the proposed framework and quantify the benefits of coupling hydroelectric and solar resources in terms of live water volume in dams and amount of solar a system may withstand with the Tana river cascade located in Kenya through an analysis of incorporating actual system data.

Short-Term Multi-Objective Optimal Operation of Reservoirs to Maximize the Benefits of Hydropower and Navigation

Traditional reservoir operation mainly focuses on economic benefits, while ignoring the impacts on navigation. Thus, the economic operation of reservoirs considering navigational demands is of great significance for improving benefits. A navigation capacity evaluation method (NCEM), which evaluates the navigation capacity considering the influence of flow velocity and water level variation on navigation, is proposed to more effectively evaluate the navigation capacity. Based on two-dimensional hydrodynamic numerical simulation, the NCEM accurately calculates the navigation capacity according to detailed flow velocity and water level changes. In addition, a short-term multi-objective optimal operation model considering the upstream and downstream navigation and power generation is established. Then, the Strength Pareto Evolutionary Algorithm (SPEA2) is used to solve the model. To verify the rationality of the method and model, they are applied to the case study of the Xiangjiaba reservoir. The results demonstrate that the method and model can not only provide a series of operation schemes for decision makers of reservoirs, but also direct the ship to pass safely through the approach channel, implying a certain practical value and significance as a reference for the short-term optimal operation of reservoirs in the future.

Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks

The high penetration of variable renewable energy sources (RESs) has greatly increased the difficulty in power system scheduling and operation. To fully utilize the complementary characteristics of various RESs, a stochastic optimization model considering the strong regulation capacity of cascade hydropower stations and the uncertainty of wind and photovoltaic (PV) power is presented. Based on the improved generative adversarial networks, the spatial and temporal correlation characteristics between wind farms and PV plants are accurately captured via measured data. Due to the nonlinear features of the hydroelectric plants, linearization methods are adopted to reformulate the original model into a standard mixed integer linear programming (MILP) formulation. Then, the model is solved with a proposed two-stage approach, in which a heuristic algorithm is used to solve the first-stage unit commitment optimization. The cascade hydraulic connection and time delay of the water flow are established in the second stage to exploit the considerably controllable adjustment capability of hydropower generation. A renewable energy base in southwest China is chosen as a detailed case study. The simulation results reveal the potential of the large-scale application of only a hydro-wind-solar hybrid system to satisfy the power transmission demand with the guidance of the coordinated operation strategy, and the performance of the hybrid system can be further enhanced with high-quality scenarios from the proposed deep neural network.

Formulation and Implementation of Short-Term Optimal Reservoir Operation Schemes Integrated with Operation Rules

To narrow the gap between theoretical research and practical application of short-term optimal reservoir power generation operation under uncertain conditions, a comprehensive study is conducted on the formulation, evaluation, and implementation of operation schemes throughout the entire procedure of optimal operation. Firstly, the three implementation modes of the optimal operation scheme are assessed with the post-evaluation method. After the optimal implementation mode is determined, the formulation and implementation of optimal operation schemes are improved by combining the advantages of conventional and optimal operation and using the concept of warning water level in operation rules. Finally, the Xiaoxuan Reservoir is taken as an example for simulation calculation under different operating conditions. The results show that the operation model integrated with operation rules has both the security of conventional operation and the profitability of optimal operation. The accordingly-formulated optimal operation schemes, when implemented with the output control mode, can provide valuable guidance for the actual operation of hydropower stations.

Coordinated Optimal Short-Term Operation of Hydro-Wind-Solar Integrated Systems

The integration of high proportion variable renewable energy sources (RES) has greatly threatened the safe and stable operation of power systems. Considering the complementary characteristics of various RES, this paper proposes a short-term optimization model for cascade hydropower stations coupled with renewable-based generations including wind and photovoltaic (PV). Based on autoregressive moving average (ARMA) model and copula function, a joint distribution model for power output of wind and PV is built with measured data and substantial scenarios are produced. Due to the nonlinear curve of forebay elevation - reservoir volume and tailwater elevation - water flow in hydropower stations, several linearization approaches are used to reformulate the optimization model into a tractable form. Hydraulic relationship and time delay of water flow between cascade reservoirs are established to make full use of the controllable complementarity of hydropower generations, and the variability of wind and PV power output can be mitigated. A basin in southwest China with wind farms and solar arrays is chosen as a detailed study case. The simulation results demonstrate the applicability and effectiveness of the proposed approach.

Short-term optimal operation of power generators taking uncertainties into account

Under the electric power market environment, the hydroelectric power plant should synthesize the economy and the technical characteristic of hydroelectric power, consider each kind of uncertainty factor in the bidding process, carry on the decomposition and the assignment in many time intervals and in many markets to reduce the overall risk, and enable the electricity generation side to achieve the high income low risk electricity generation goal. In view of the single time interval risk measure’s insufficiency, this paper proposes one new kind of multi-time interval water electricity system optimization scheduling model. This model takes the total risk of multi-time to be low as a goal under the electric power market condition, simultaneously considers the overall price fluctuation and the uncertainties of water to generation side economic efficiency influence, and takes into account the storage capacity of hydropower stations, discharge, the unit output and other constraints, in order to follow the optimal scheduling scheme. Simulation results show that the model can reflect the essential characteristics of market risk under the spatial and temporal distribution of water electricity, and ensure the system safe and reliable and the smallest risk, so as to provide a new way of thinking of optimizing the operation decision-making and risk assessment of hydropower generation company.