Optimal Short-term Scheduling Research Articles

Short-term optimal scheduling of wind-photovoltaic-hydropower-thermal-pumped hydro storage coupled system based on a novel multi-objective priority stratification method

In the new power system with high proportion of uncertain renewable energy sources (RES), there is a defect of RES consumption at the expense of other power sources' operational efficiency. This paper proposes a short-term optimal scheduling model of wind-photovoltaic-hydropower-thermal-pumped hydro storage (WPHTPHS) coupled system, which realizes the multiple optimization objectives involving minimizing operation cost of thermal power units, maximizing clean energy power generation, minimizing net load fluctuation and thermal power regulation. First, to overcome the dimension disaster problem in the solution space of high-dimensional random variables, a method for pre-solving integer state variables is proposed. Then, a novel multi-objective solution strategy of priority stratification-coupled feedback combined with improved plant growth simulation algorithm is designed. Finally, the effectiveness and superiority of the proposed model and solution method are demonstrated by case studies, and the numerical results show that the number of startups and shutdowns, standard deviation of output and operating cost of thermal power units are reduced by 90.9 %, 65.34 %, and 14.01 % respectively, compared with traditional wind-photovoltaic-thermal strategy. This study contributes to resolving the relationship between conflicting objectives and highlighting the potential advantages of WPHTPHS coupled system to maximize overall performance from economic and stability perspectives.

Short-Term Optimal Scheduling of Power Grids Containing Pumped-Storage Power Station Based on Security Quantification

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model.

Thermodynamic modeling and optimal short-term scheduling of a green H2-fueled combined heat and power generation system for industrial zones

This paper presents a novel green combined heat and power generation system for industrial applications. It composes of a hydrogen-fueled combined cycle power plant and a battery energy storage. Firstly, the hydrogen-air rich mixture enters the combustion chamber of the gas turbine cycle for generating electricity, while recovering the heating energy from the exhausted flue gases for driving a steam turbine cycle. The heating energy extracted from the condenser heat exchanger is also used to supply the heating demand of the end-user. Moreover, a comprehensive thermodynamic model and analysis of H2 combustion process is provided to estimate the adiabatic flame temperature based on enthalpies of reactants and products. The optimum operating point of the cogeneration system is found by solving a mixed-integer nonlinear problem using MATLAB and GAMS over a 24-h study horizon aiming to minimize its daily operation cost considering all technical constraints of system components and load-generation balance criterion. It is found that the energy efficiency of the hydrogen-fueled combined cycle changes from 46 to 58 % while generating 11.331–12.982 MW electricity and 4.436–4.984 MW heat. Two cases are studied without and with participation of battery in energy procurement strategy. It is demonstrated that the objective function of the optimization problem improves from 18 $ cost in case 1 to 29 $ revenue in case 2.

Short term hydropower scheduling considering cumulative forecasting deviation of wind and photovoltaic power

The cumulative forecasting errors of wind and photovoltaic (PV) power pose serious challenges to the short-term scheduling of hydropower stations that connected to the same regional power grid. The variable and intermittent nature of wind and PV power necessitate a flexible power source to achieve power balance. Consequently, hydropower, with its flexible adjustment capability, plays a crucial role in supporting the consumption of wind and PV power stations in short-term scheduling. Meanwhile, to address the grid's peak shaving requirement, the scheduling of cascaded hydropower system becomes increasingly complex. Given the limitations of current wind and PV power output prediction technologies, achieving precise forecast remains challenging. In practice, the actual outputs of wind and PV power stations often diverge considerably from predicted values over multiple consecutive periods. This variability may result in the underutilization of wind and PV power if the reserve capacity of hydropower stations is insufficient. Therefore, it is essential to account for the cumulative deviation in electricity from wind and PV sources. By computing probability distribution and establishing boundary condition of the cumulative deviation, the operation of the cascaded hydropower system can be controlled more effectively, thus enhancing the stability of a multi-energy complementary system. This study employs Gaussian Mixture Model (GMM) to characterize the probability distributions of cumulative electricity deviation across multiple periods of wind and PV power and takes it as restriction to regulate the operation of hydropower stations. In order to balance the deviation electricity, the downward electricity reserve, upward electricity reserve and the storage energy of the cascaded reservoirs are introduced as restrictive conditions. The proposed Hydro-wind-PV peak shaving model is then applied to the short-term optimal scheduling of the Lancang River cascaded hydropower system to validate the feasibility and efficacy.

Short-term load distribution model for giant cascade serial diversion-type hydropower stations

With the development of complex cascade serial diversion-type hydropower plants (CSDHP) in Southwest China, it has become imperative to study short-term scheduling challenges that are distinctive to this type of hydropower facility. Unlike conventional cascade hydropower stations, which have large reservoirs to regulate the outflow between stations, the CSDHP system consists of several diversion-type hydropower plants connected in series, requiring strict matching of turbine discharge between upstream and downstream facilities. In addition, these plants impose complex operation constraints, such as multiple units sharing one tunnel, which greatly increases the difficulty of short-term scheduling. This paper proposes a short-term optimal scheduling model for CSDHP systems aimed at achieving efficient load distribution among units while minimizing water consumption. The model takes into account the strict matching of turbine discharge and the complex constraints of multiple units sharing one tunnel. Subsequently, the model is transformed into a mixed integer linear programming problem using specified linearization techniques, with complexity further addressed via an iterative resolution method. A case study of a cascade illustrates the model’s efficacy in facilitating effective unit load distribution under complex hydraulic and electrical constraints, which substantially reduces the system’s water consumption by up to 6.1% during the dry season and 1.8% during the flood season, and reducing the number of crossing forbidden zones by 50.0% and 52.0%, respectively. The result shows the models’ potential to enhance day-ahead scheduling for the CSDHP system.

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.

WITHDRAWN: Short term optimal scheduling of a virtual power plants integrated with electrothermal hybrid energy storage system using an effective adaptive multi-objective differential evolution algorithm

virtual power plants (VPPs) in the power distribution grid play an important role in balancing the power and heat generation and consumption in residential, commercial, and industrial sectors. This study proposes VPPs for risk-averse energy exchange of resources and customers in the presence of the hybrid storage system. An appropriate energy management program is presented to make a balance between generators and consumers in VPPs considering generation uncertainties including electricity demand and price forecasting in the electricity market and renewable energy uncertainties. Hence, monetary exchange methods such as day-ahead agreements are proposed for energy exchange among VPPs. The expanded exchange structure of the suggested framework simplifies the renewable energy's sources to the electricity market despite its small-scale and non-dispatchable features. Also, a novel model is presented for short-term programing of virtual power plant considering technical and economic uncertainties. Given that VPPs can enter the electricity market based on time-of-use (TOU) pricing strategy and participation in demand response (DR) program, minimizing the expected cost of energy generation and environmental pollution is considered as the objective function. A multi-objective efficient model of energy management employing the machine speed scaling mechanism is suggested. An effective adaptive multi-objective differential evolution (AMODE) algorithm is presented to solve the multi-objective optimization problem. AMODE usages a novel Fitness Evaluation Mechanism (FEM) using dynamic reference point and fuzzy association randomness study to evaluate solutions in the evolutionary population by implementing the conditional value at risk (CVaR) method for risk management. The simulation studies and sensitivity analysis results are evaluated to confirm the effectiveness of the proposed model on a VPP in the island and grid-connected modes with and without participation in DR program. Finally, the conceptual results are discussed.

Optimal operation scheduling of a microgrid using a novel scenario-based robust approach

This article presents the optimal short-term scheduling of an integrated heat and power microgrid (MG) considering load uncertainty. The MG comprises combined heat and power (CHP) units, a small thermal power plant, a small boiler, an electrical energy storage system and a thermal storage tank, and can further engage in electrical energy transactions with the upstream network regarded as the day-ahead market. Given the uncertain nature of the electrical and thermal load, a novel scenario-based robust optimization framework is developed to overcome the problem uncertainties. The effectiveness of the proposed scenario-based robust scheme against conventional deterministic and stochastic scheduling methods is verified through numerical simulation on a prototype MG. The results suggest that the proposed approach can effectively forecast the uncertainties in MG load and provide the operator with more realistic results to handle the consuming load uncertainty.

Research on short-term optimal scheduling of hydro-wind-solar multi-energy power system based on deep reinforcement learning

Recently, grid-integrated wind power and photovoltaics have experienced rapid growth. However, their uncertainty increases the difficulty of grid scheduling and operation. Short-term power generation decisions made by conventional scheduling methods, which are based on the output forecast information of wind and solar power often impact the power benefit owing to forecast errors during the actual operation. This study explores the application of deep reinforcement learning methods for short-term optimal scheduling of hydro-wind-solar multi-energy power system, considering forecast uncertainty. First, with the objective of maximizing power generation benefit from the multi-energy complementary system, the Deep Q Network (DQN) method in deep reinforcement learning is employed to construct the model framework of the short-term optimal scheduling of hydro-wind-solar multi-energy power system (collectively referred to as DQN model later). Thereafter, the impact of each learning parameter in the DQN model on performances is analyzed, and the model parameters are reasonably configured. Furthermore, the DQN model is driven by wind and solar power output forecast data to develop short-term power generation decisions. Finally, the actual wind and solar power output is used as input to implement short-term decisions, whereas the dynamic programming (DP) method is used to build the model as a comparison to evaluate the power benefit and computational efficiency of the DQN model. The multi-energy complementary system of hydro, wind, and solar power of the Jinping-1 Hydropower Station in the Yalong river basin is used as an example for the study. In the flood season, compared with the DP model and the historical actual operation process, the total power generation benefits obtained from the DQN model's implementation of short-term decision-making increases by 6.18% and 1.38%, respectively. In the dry season, the total power generation benefits obtained from the DQN model's implementation of short-term decision-making increases by 2.26% and 19.00% respectively compared with the DP model and the historical actual operation process. Additionally, the DQN model can significantly improve the decision-making efficiency. The DP model requires minutes or hours of computation time; the DQN model consumes less than 1s to compute, which is conducive to efficient decision making of the complementary system.

The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps

The coordinated scheduling of the cascade hydro-photovoltaic hybrid system can significantly alleviate the adverse impact from the intermittence of solar energy resources and hence improve the overall stability of bundled power output. The traditional short-term scheduling strategy of cascade hydro-photovoltaic systems is generally based on a fixed time step, which cannot efficiently adapt to the ever-changing photovoltaic output. In this paper, a novel artificial intelligence-assisted short-term scheduling model with hybrid time steps is proposed, where the time-frequency characteristics of photovoltaic output are taken into consideration. The photovoltaic output is estimated by a data-driven long short-term memory network. The Hilbert-Huang transform is used to analyze the time-frequency characteristics of the photovoltaic power. The hybrid scheduling time steps are determined according to the analyzed instantaneous frequency of photovoltaic power. A short-term optimal scheduling model based on data-driven photovoltaic output evaluation and hybrid time intervals is established to minimize the operational fluctuation of hydropower units and to track the planned output curve simultaneously. Finally, a comprehensive index involving the safety, economy, and operational efficiency is established to compare the overall performance of the hybrid time step model and that of the fixed time step models. The results demonstrate that the proposed hybrid time step model yields the lowest comprehensive ranking score of 1.00, obviously better than the figures for the fixed 60-minuite step model (2.86) and the fixed 15-min step model (2.20). It indicates that the proposed model can achieve the optimal compromise of overall performance via adaptive scheduling time steps.

Short-term optimal scheduling of cascade hydropower plants with reverse-regulating effects

The joint operation mode of large hydropower plants and their reverse-regulating plants has been widely used in China's major river basins. Hence this paper establishes an optimization model for the short-term optimal scheduling of cascade hydropower plants with reverse-regulating effects. Two conflicting objectives that need to be considered for optimal operation are minimizing the peak-valley difference of the power grid and minimizing the outflow variation of the reverse-regulating plant. The complex hydraulic coupling between the main-regulating plant and reverse-regulating plant is of particular concern. A novel two-layer nested approach, coupling constraint method and a mixed-integer linear programming (MILP) approach, is therefore proposed to solve the model. The results for three case studies demonstrate that: 1) The proposed approach is computationally efficient, and the average time for a single calculation is about 12 min; 2) The peak-valley difference of power grid G in dry season and flood season is reduced by 26.0% and 15.7%, respectively, after optimization. The outflow variation of the reverse-regulating plant has been controlled at 300 and 350 m3/s, respectively. 3) The developed model produces a more realistic and executable scheduling scheme than the benchmark model which does not consider the complex hydraulic coupling between cascade plants.

Double dam coordination model based on Lake Mead and Lake Powell

With climate change, water supply from dams and reservoirs is decreasing in many regions. For centuries, people have built dams on rivers and streams to hold back water, and reservoirs have been built as a means of managing water supply. In this paper, we established a double-dam coordination and cooperation model. We abstracted the lake into a cuboid, and established a physical model of hydropower supply and demand based on the water levels of Lake Mead and Lake Powell. Next, we established a hydropower conversion rate model, taking into account The cooperation between the power stations has established a short-term optimal scheduling mathematical model of the hydropower system. Through the constraints, we have reached the relationship between the hydropower conversion efficiency and the lake water level. We established a short-term optimal scheduling model for hydropower systems, which addresses t he competing interests of general agricultural, industrial residential water use and water availability for water power and electricity production. The electric energy is the largest and the unification of the rules that the electric energy is the largest and the water consumption is the smallest is realized. Combined with the water consumption constraints of the target power station, the water balance constraints of the reservoirs of each hydropower station, and the reservoir capacity constraints, the water level height reduction model is established. On the basis of the previous model, we established an optimization model based on particle swarm optimization, and brought it into matlab for simulation through particle swarm optimization, and calculated the hydropower conversion efficiency of Lake Mead and Lake Powell with the height of the water level under constraints.

Short-term optimal scheduling of cascade hydropower plants shaving peak load for multiple power grids

Cascade hydropower plants which have good regulation performance and are managed by the dispatching center of regional power grids are usually required to simultaneously shave the peak load for multiple provincial power grids, which is an important way of relieving the growing peak shaving pressure on power grids in central and eastern China. This paper establishes an optimization model for the short-term generation scheduling of cascade hydropower plants in regional power grids. In this model, minimizing the peak-valley load difference of multiple power grids is adopted as the objective function. In addition to conventional hydraulic constraints, the operation constraints of individual hydropower units, electrical constraints and head effect on the power generation are all considered to obtain precise scheduling. The original nonlinear and non-convex model is converted into a standard mixed-integer linear programming (MILP) formulation through several linearization strategies. The results for the real-world case study indicate that: 1) the proposed model is computationally efficient with a calculation time of 326 s; 2) the peak-valley differences of the Shanghai Power Grid and Zhejiang Power Grid decreased by 9.71% and 2.29%, respectively; 3) compared with actual operation, the proposed model shows better performance in peak shaving for multiple provincial power grids.

A grasshopper optimization algorithm for optimal short-term hydrothermal scheduling

The optimal generation for short-term hydrothermal scheduling (OGStHS) with the deliberation of various purposes is a complex non-linear constrained optimization problem. There exist numerous constraints, which make the OGStHS optimization problem more complicated. The considered constraints for this problem are mostly related to energy performance, operational conditions, water, and power infrastructure. All these constraints would generally influence the cost of fuel. In this study, a multi-objective optimization form of OGStHS is suggested to estimate the minimum cost of fuel, which mainly influences industrial operation. The water transfer delays among multi-related reservoirs and the thermal plants’ valve-point influences are considered for the accurate formulation of the OGStHS problem. Meantime, a grasshopper optimization algorithm (GOA) is performed to handle the OGStHS problem by getting optimized for both objectives concurrently. A modern approach is shown in this study to get a solution to the OGStHS problem. Furthermore, to deal with the complex restraints efficiently, modern heuristic restriction treatment processes with no drawback impact frames have been offered in this study. Two hydrothermal power systems have illustrated the suggested GOA technique’s utility and performance. Compared with other available approaches, the analytical results are admitted that GOA can provide a better understanding by decreasing fuel cost and emission concurrently.

A Short-Term Optimal Scheduling Model for Wind-Solar-Hydro Hybrid Generation System With Cascade Hydropower Considering Regulation Reserve and Spinning Reserve Requirements

In order to meet the challenges brought by the high penetration of intermittent and fluctuating wind and solar power, a short-term optimal scheduling model for wind-solar-hydro hybrid generation system with cascade hydropower is established with the objective of minimizing the amount of abandoned wind, solar and hydro power and maximizing the stored energy of hydro stations. Cascade hydropower is considered to provide spinning reserve and regulation reserve to ensure the security of system. Mixed Integer Linear Programming (MILP) method is used for the short-term optimal schedule of wind-solar-hydro hybrid generation system. The case studies show that spinning reserve and regulation reserve are beneficial to the hybrid generation system, and verify the practical applicability of the proposed model.

Optimal Scheduling Strategy of Cascade Hydropower Plants Under the Joint Market of Day-Ahead Energy and Frequency Regulation

In order to adapt to new situation of cascade hydropower enterprises (CHEs) participating in the joint market of day-ahead energy and frequency regulation (FR), a short-term optimal scheduling strategy for cascade hydropower plants (CPPs) is proposed. Firstly, the development situation of AGC in China is introduced and the CPPs’ AGC mode of participation in FR from the perspective of economy and solvability is analyzed. Secondly, for maximizing the revenue, the economic dispatching model of CPPs based on the two-part FR compensation mechanism is proposed, which combines the benefits of both the energy and FR. Finally, a two-stage optimization method that couples the load distribution among multi plants with the economic operation of a single plant is proposed for solving the model above. The experimental results show that the two-stage optimization method has good convergence and robustness in solving the proposed model, and compared with the single energy model, the total benefits of the proposed model increases by $ \$ $ 72935.6 (5.54%) through optimizing the distribution of electricity quantity and AGC capacity, which provides a new idea for cascade hydropower enterprises (CHEs) to explore market dividend and optimize market decision-making.

Joint Optimal Operation of Wind Power Plant and Cascade Hydropower Station

With the depletion of fossil energy, the whole people advocate energy conservation and emission reduction, making the scale of wind power integration increase. While wind power has fluctuating and intermittent characteristics, this paper develops a short-term combined operation strategy of wind and water using the flexible regulation characteristics of cascade hydropower stations. To ensure full access to wind power, reduce the impact of wind power fluctuations as little as possible. A multi-objective short-term optimal scheduling model with the minimum variation of combined wind and water output and the largest total power generation is established considering the constraints of storage capacity, discharge flow, reservoir water level, output of each power station, and power transmission section. Aiming at the shortcomings of traditional particle swarm optimization algorithm, such as slow convergence rate and fall into optimal local solution easily in the late search, dynamic inertia weight and learning factors are introduced and applied to model solving. The simulation results show that the cascade hydropower station can effectively stabilize the wind power fluctuation, improve the wind power consumption level, increase the power generation of the system, and ensure the safe and stable operation of the power system.

A Short-Term Optimal Scheduling Model for Wind-Solar-Hydro-Thermal Complementary Generation System Considering Dynamic Frequency Response

This paper proposes a model to realize the coordinated optimal dispatch of wind-solar-hydro-thermal hybrid power generation system, aiming at minimizing the power generation cost of thermal generators and maximizing the water storage value of hydropower stations at the end of the scheduling periods, while considering the dynamic frequency response of wind/solar/hydro/thermal generators. Considering the virtual inertia and droop control of wind farms and PV stations, the dynamic frequency response model of wind-solar-hydro-thermal multi-energy complementary system is derived and the metrics that evaluate the frequency dynamic characteristics of the generation system are presented. Then the dynamic frequency response constraints are incorporated into the traditional optimal scheduling model and the Mixed Integer Linear Programming (MILP) method is used to solve it. Finally, the validity and applicability of the proposed model are verified by simulation examples.

Optimal short-term hydro-thermal scheduling using multi-function global particle swarm optimization

<p>An optimal short-term hydro-thermal scheduling (ST-HTS) problem is solved in this paper using the multi-function global particle swarm optimization (MF-GPSO). A multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The ST-HTS problem determines the optimal power generation of hydro and thermal generators which is aimed to minimize total fuel cost of thermal power plants during a determined time period. Effects of valve point loading and prohibited operating zones in the fuel cost function of the thermal power plants is examined. Power balance, reservoir volume, water balance and operation constraints of hydro and thermal plants are considered. The effectiveness and feasibility of MF-GPSO algorithm is examined on a standard test system, and the simulation results are compared with other algorithms presented in the literature. The results show that the MF-GPSO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques reported in the literature.</p>

Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm

This research paper reports the implementation of short-term scheduling of hydro-thermal power plants by using an artificial bee colony algorithm. Short-term hydro-thermal scheduling is a type of economic dispatch problem in which thermal power plants are dispatched at the optimized operating point to reduce the fuel cost and to achieve in parallel the maximum cost-benefit from hydel power plants. The power system, considered in this study, is assumed to run optimally and the transmission losses have also been taken into account. The artificial bee colony algorithm proves itself to be most suited for this particular problem as it results in the minimum cost in the shortest time compared with those obtained from previously applied techniques.