The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.
Case Study | Limitation of Parameters | Optimization Techniques | Consideration of Main Goal | Ref. |
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44 units, China | Balance, discharge, delay period, and outflow of water; reservoir storage volume; generation. | MILP method | Maximize the utility of energy production during the outlining horizon. | [24] |
Portuguese | Water conversion of the reservoir; head, storage, discharge, and spillage of water; power generation. | MINP method | Employed to model the on-off behaviour via integer variables to avert inflows at prohibited regions. | [25] |
Two cases, Portuguese | Parity and disparity constraints or unpretentious variables of restrictions. | A mixed-integer quadratic programming method | Model on–off behaviour to obtain realistic energy, without affecting future operations. | [26] |
Portuguese | Balance, head, storage, discharge, and spillage of water; power generation. | A non-linear approach | Considering head-dependency. | [27] |
Norwegian industry | The uncertainty of water inflow and upcoming costs. | Stochastic successive linear programming | Employed a first-order approximation to the optimization of water head. | [28] |
34 hydro units, China | Level and hydraulic coupling of reservoirs; release and the flow of water; power production. | Successive approximation approach | The constant difference for a delay period of water to define operations realistically exhaustive. | [29] |
Gezhouba and Gorges, China | Water discharge; hydraulic head; online/offline time; reservoir water level. | Culture algorithm with differential evolution | Maximize the electrical power generation through an entire dispatch interval. | [30] |
Three Gorges–Gezhouba, China | Balance, discharge, and head of water; power balance; uptime/downtime; turbine-generator capacity; reservoir storage volume. | Hybrid multi ant colony system with adaptive deferential evaluation | Locate which unit ought to be on and the standards at which to produce energy in per unit to match the specific energy request with full water consumption. | [31] |
Slovenia | Min and max for reservoir volume; permissible variation in the reservoir; production energy; discharge. | Parallel Self-Adaptive Differential Evolution | Optimal production distribution via minimizing the utilized water volume in each generated unit. | [32] |
Benchmark of two examples | Hydropower generation; dynamic balance and discharge of water; reservoir storage volume. | A hybrid chaotic genetic algorithm | Discovery of the optimum hydro generation units in each hour to employ the restricted resource of water. | [33] |
Hubei, China | Dynamic balance and discharge of water; reservoir storage volume; hydropower generation. | A self-adaptive chaotic with PSO | The optimal dispatching is by maximum generation considering the security conditions and reliability. | [34] |
Yunnan, China | Installed capacity utilization hour; hydropower generation. | Genetic algorithm with support vector machine | Power generation energy prediction. | [35] |
Three-gorge dam, China | Maximum volume of water discharge; initial level in the water reservoir. | Developed a genetic algorithm. | Establish the operation principle values for optimal decisions. | [36] |
Saguenay-Lac-St-Jean, Canada | Unit commitment and loading problem; hydro generation; turbine-generator efficiency; gravity acceleration; turbine net head and water discharge. | Dynamic programming | Dispatches energy production among units and explores to optimize gross generation and select the unit commitment and make discipline unit start-ups. | [37] |
Qing River, China | Uncertainties of inflow containing its local and upstream outflow; temporary power instructions. | Self-Optimization System Dynamics | Operation including real-time. | [38] |
Sichuan, China | Balance, storage capacity, and outflow of water; expected output. | Multi-Stage Dynamic Programming method | Uses maximum power generation criterion to establish reservoirs optimal operation. | [39] |
8 stations, China | Volume, head of water; reservoir storage volume; power output; dealing within/non-equality. | Electromagnetism-like algorithm. | Realize the optimal power output and to define its relationship with the existing level of water. | [40] |
State Grid of China | Energy loads per grid; primary storage of reservoir; domestic inflow of reservoir; energy production; storage of reservoir; turbine inflow and spill. | Local search algorithm | Acquire nearer to the OGS for a group of hydropower units on some rivers and transmit produced energy to some energy grids. | [41] |
Xiluodu and Xiangjiaba, China | Hydraulic connection; reservoir storage; water discharge and balance; forbidden operating areas; limits of hydropower system; uptime/downtime. | Developed binary-real bee colony optimization algorithm | Minimize the gross water exhaustion, taking into account enough demands of load and different restrictions. | [42] |
Québec, Canada |
Water reservoirs; what comes in and out of the rivers and the transit capacity in the river divisions; possible delays; head and flow of water; production. | Fast Near-Optimal Heuristic | Maximize the stored value of water in the reservoirs at the scheduling end, maximize the final water quantity and control the variations in turbine discharge. | [43] |
Norwegian watercourse | The inflow uncertainty function when setting the maximum values of bids. | Heuristic algorithm. | Demonstration of how prototypes can be expanded to grant a maximized curve of bids. | [44] |
Block diagram | Load balance; spillage modeling; water flow and reservoir storage volume; turbine net head. | Two-phase neural network | Minimize the production costs for non-hydraulic power through the period of schedule. | [45] |
Qingjiang, China | Load balance; balance and storage of daily water; daily average and limits for power output. | Multi-objective optimization model | Maximizing the stored power in the hydropower units and minimizing the gross discharge of water. | [46] |
Douro River, Portuguese | Flow and head of water. | The Linprog Function | To set hydropower plants as price producers to get a more practical model. | [47] |
Case Study | Limitation of Parameters | Optimization Techniques | Consideration of Main Goal | Ref. |
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Zagunao River, China | Peak shaving; equations of water; spinning-reserve; uptime/downtime; limits of the generator and prohibited operating zones. | Discrete differential dynamic programming | Acquire additional benefit for power generation with a confirmed water volume based on the real requests of the energy grid. | [48] |
Numerical simulation example | Hydropower production; turbine inflow; the net head of the reservoir; delay period for the water transfer. | An enhanced differential evolution algorithm; chaos theory | Minimize the variation summation between the gross generation of hydropower system and the load request per hour during the period of dispatching. | [49] |
Numerical simulation | Load balance; limits of generation; water discharge; reservoir storage volumes; transport delay time. | Enhanced PSO algorithm | Minimize the gross expenses while utilizing the accessibility of the hydro exporter as far as possible. | [50] |
Brazilian Power System | Generation and outflow of the hydro plant; reservoir storage volumes; water dynamic balance. | Adjusted PSO algorithm | Maximize the gross hydropower production to meet different material and operational constraints. | [51] |
Case Study | Limitation of Parameters | Optimization Techniques | Consideration of Main Goal | Ref. |
---|---|---|---|---|
Nord Pool, Norway | Reservoir balance of water; upper and lower limit of generation, contract, and reservoir; spillage. | Stochastic linear and non-linear programming. | To determine the OGS and the extent of binary contracts. | [52] |
Greek Power System | Uncertainty of turbine discharges, load request, and rivals’ quotes. | Stochastic mixed-integer linear programming. | To optimize financial revenue and making use of manipulating market costs. | [53] |
Portugal | Balance, head, storage, discharge, discharge ramping, and spillage of water; power generation; commitment. | Mixed-integer non-linear programming. | Realize the best quotes by determining the plans of bids in the daily markets. | [54] |
Norway | Contents and spillage of the reservoir; water flow pumping capability; demand and supply of power. | Stochastic DDP. | Establish system operation and contribute to minimizing the expected future operational costs. | [55] |
Swiss hydro system | Taking part in the over-the-counter, power futures, options, day-ahead, and spot markets. | Stochastic dynamic programming. | Optimization depending on hourly price forward curve. | [56] |
Swiss hydro system | Upper and lower basin level and water inflows; the water levels in the basins have negligible influence. | Integrating ancillary services. | An optimal offering of secondary control of cost-taker hydropower generators with pumped storage. | [57] |
Swiss hydro system | Processes of avoiding risk, saving of stores for spinning, and hydropower generation flexibility. | Stochastic DDP. | Discovery of realistic quantities of water that was supported by national legal cuts. | [58] |
Norwegian watercourse | Inflow handling to reservoirs, their volumes, hydro energy costs. | Stochastic DDP. | Determine equivalent involvement in the daily ability markets and its reserve. | [59] |
Lysebotn, Norway | Balance of energy and reservoir; springing reserve, startup cost; hydro coupling; power discharge function. | Stochastic DDP. | Fulfil the hydropower units operators’ demands to get steady operation for the grid. | [60] |
Parnaiba river, Brazil | Storage, discharge from of bounds on the reservoir; initial volume and target volume; hydraulic generation. | Two-phase optimization neural network. | Minimize the overall production cost while satisfying the load demand. | [61,62] |
Guilan, Iran | Accessibility of energy production units; obtainable water in hydropower units reservoir. | Possibilistic programming approach. | Set the production, selling and purchasing units of generation company for the following season. | [63] |
Case Study | Limitation of Parameters | Optimization Techniques | Consideration of Main Goal | Ref. |
---|---|---|---|---|
Yellow River, China | Annual consumption, release, and storage of water; cost structure. | Constrained Markov decision process | Determining the water release and to minimize the total energy production cost. | [64] |
Hydro plants, Brazil | Hydro generation; head, discharge, and density of water; gravity acceleration; average efficiency. | Markovian stochastic DP | Minimizing the predictable quantities of the operating expense by considering discharges. | [65] |
Sobradinho, Brazil | Time; cost; load demand; efficiency; discharge and head from turbine; spillage; forebay/tailrace function. | Markovian stochastic DP | Monthly inflow for single-reservoir hydropower systems. | [66] |
Røldal/Suldal Scandinavia | Balance of water and reservoir; contract balance of future period, spot market, and accumulation of profit. | Stochastic DDP | Obtain a firm’s risk management to maximize an outlined interval separable advantage task. | [67] |
Norsk Hydro, Norway | Modified transition probabilities; cost node numbers; the medium cost in a period time of stage for cost node. | Stochastic DDP approach | To assess the transmission prospects for cost from the previous week and beyond. | [68] |
Yalong River, China | Min/max level of release and storage for the reservoir at the overall/end of time; max/min of generation. | TC; CM; HM; MCS; stochastic DP | Generate energy and sell with the best revenue with minimum market risks. | [69] |
Tokke Sys., Norway | Equations of water balance; reservoir capacity limitations; inflows of water for each reservoir at plants. | Stochastic DDP | To solve an inherently stochastic problem because of the uncertainty upcoming discharge of the reservoir. | [70] |
South-west, Norway | Reservoir balance; energy balance including inflow and generation; start-up expenses; the amount of capacity available for sale; primary frequency reserve. | Stochastic DDP | To produce a performance metric of the revenue assignment to reach convergence. | [71] |
Jiangxi, China |
Balance, level, and the outflow of water; power output; non-negative constraints. | Progressive optimization algorithm | Optimal reservoir scheduling to completely utilize water exported and make it economical. | [72] |
Xiangjiaba, China | The capacity of reservoir storage; head and inflow of water; power generation; hydro plant network. | Improved parallel progressive optimality | Maximize the gross energy production of entire hydro plants throughout the dispatching time. | [73] |
Nanpan River, China | Storage volume and discharge of reservoir; power generation; water balance. | Chaos in the GA | Maximize generation output based on the reservoir discharges chronologically. | [74] |
Three Gorges, China | Balance, discharge, and the level volume of water; capacities of reservoir storage; the level of river water; hydro generation. | Chaotic maps in the PSO algorithm | Maximize the gross revenue of the energy production and distribution during a long period. | [75] |
Himreen lake dam, Iraq | Net head of turbine; flow rate and density of water; hydropower system efficiency. | Firefly algorithm and PSO | To estimate optimal discharge of water of hydro reservoirs and energy production per unit. | [76] |
Himreen lake dam, Iraq | Net head of turbine; flow rate and density of water; hydropower system efficiency. | Series division method with FA and PSO | To estimate optimal discharge of water of hydro reservoirs and energy production per unit. | [77] |
Three Gorges, China | Balance, level, and discharge of water limits; power generation limits. | Multi-Core Parallelization of PSO | To discover the optimum plan for maximum power generation through the operation interval. | [78] |
Three Gorges, China | Level, head, discharge, and balance of water; reservoir storage conversion; output generation. | Multi-objective adaptive differential evolution | Minimum environmental shortage and excess water capacity; maximum energy production. | [79] |
Jinsha River, China |
balance, level, head, and outflow of water; hydraulic connection; storage reservoir. | Multi-population ant colony optimization | The maximum utility of energy production of big cascaded hydropower plants. | [80] |
Three Gorges, China | Hydraulic connection; output limit; water limits of balance, release, level, and reservoir. | An adaptive artificial bee colony algorithm | Maximize the gross utilities of energy production by finding the optimal procedure of the water level rate. | [81] |
Three Gorges Dam, China | Hydraulic connection; level, release, and dynamic balance of water; reservoir water level; output power. | Multi-objective artificial bee colony algorithm | Optimize both generation benefits and firm output simultaneously. | [82] |
Southeast river, Brazil | Net head of water storage as a non-linear function, spillage, and inflow. | Predictive control | To exemplify hydro energy production by using deterministic optimization model. | [83] |
Paranaíba River, Brazil | Net head of water storage as a non-linear function, spillage, and inflow. | Predictive control | Provide an inflow sequence and supply the optimal inflow solutions throughout a specific period. | [84] |
UNICAMP, Brazil | Operating costs; generation; head and discharge of water; release and balance of the reservoir; spillage. | Adaptive model predictive control | Provides optimal releases and optimizes operation costs plus the minimum future operation costs. | [85] |
Block diagram | The capacity of the reservoir; minimum and maximum for storage and discharge. | Tabu search algorithm | Predictable value of the water residual in the reservoir, optimize power generated, and water conservation. | [86,87] |
Spain | Independent, linear and quadratic coefficients, and the predicted value operator of the probabilistic production expenses; generation; the flow per specific commodity | The non-linear network flow technique | Minimizing the total predictable production expenses per period, considering the water inflows per period. | [88] |
Norway | Maximum and time of generation: minimum and maximum level of the reservoir; spillage; the value of storage. | Successive linear programming | How is scheduling mixed in the new arrangement for market-clearing and system operation? | [89] |
Leirdøla, Norway | Volume available capacity of bid; water flow rate; generated power; the day-ahead; balance, level, and bounds of the reservoir; start-up and shutdown costs. | The multistage stochastic mixed-integer programming model | Generate bid curves as this is the only output that depends on the expectation on future prices rather than the actual realizations. | [90] |
Nord Pool, Norway | Min and max level, production, spillage, and Inflow of reservoir; electricity price; water discharge. | Linear Decision Rules | Obtain optimal use of resources and the expected discounted market value of total production. | [91] |
Miño-Sil River, Spain | Hourly water inflows and head; reservoir level; generation; costs of wear and tear, start-up/shut-down, and energy; environmental flows; ramping rates. | Mixed-integerlinear programming | The uninterruptible discharge between sequential weeks is warranted via accreditation of the inflows per hour as a variable in the yearly problem. | [92] |
Southern, China | Electrical energy balance; interruptible load; generating; head, flow, storage, and balance of water. | Mixed-integer programming method | Minimize the cost caused by various power interruption measures. | [93] |
Kashmir and Jammu, India | Average power production; specific weight, flow, and net head of water; efficiency of turbine and generator. | Decision support system | Improve operational efficiency and make optimal operational and trading decisions. | [94] |
Three Gorges, China | One/two-period formulation depends on single-period utility includes (reservoir volume storage; inflow and release of water) and maximum cumulative utility. | Marginal utility principle | Determine the optimal delay of storage among intervals that set the proposed concept in water equipping. | [95] |
Francisco River, Brazil | Storage, spillage, and discharge of water; upstream plant. | Simulation model | Evaluating the simulation efficiency of the hydropower model. | [96] |
The optimal generation scheduling (OGS) of the hydro system is resolved by the employment of various optimization algorithms, which include the heuristic optimization approaches. The description of the objective function of the LMSTHS optimization problem shows the numerous parities and disparities related to hydro generation systems. A renewed and complete survey of the optimization method implementation for the hydro scheduling solution is given in this article, which examines approaches from various perspectives. In this article, the fundamentals of various optimization algorithms for solving the hydro scheduling problem are studied, and special parameters of the algorithms are included. Many methods take into account the statistical analysis of the acquired solutions of the OGS of hydro units, in which several case studies are considered. The article, which describes various optimization approaches to the hydro scheduling problem, considers the qualitative and statistical comparison of the approaches. It may considerably benefit the academic authors in the field of solving the LMSTHS problem limited by the execution of optimization approaches. The solution to the OGS of hydro and thermal systems in alternating current power flow is a more practical problem that may be presented as future research in the field. The scheduling of hydro systems would be more necessary and valuable by considering other sustainable energy resources like wind and solar power, which are currently manipulated by the employment of optimization approaches. The impact of pumped water storage on the solution of LMSTHS problem has additional study potential, which may be investigated in future work.
This entry is adapted from the peer-reviewed paper 10.3390/en13112787