Operation Of Hydrothermal Systems Research Articles

A nonlinear multi-period hydrothermal optimal power flow model for hydropower systems

Despite their social and climate benefits, renewable energy sources also present challenges such as intermittency, volatility and randomness, which may considerably reduce security and reliability of the system. Hydro units have the ability to provide flexible and rapid energy reserves, serving as an effective way of handling these challenges. Yet, many Hydrothermal Optimal Power Flow (HOPF) models in the literature often provide a simplified and sometimes inaccurate representation of the hydraulic aspects. In most HPOF models proposed in the literature, the representation of hydraulic aspects disregards important physical and operational hydro constraints. Such simplifications can curtail the potential benefits of hydro generation, especially concerning system security and reliability. In this paper, we investigate the impact of hydro constraints in the operation of hydrothermal systems by means of HOPF models. For such a purpose, we propose a nonlinear multi-period active–reactive HOPF model where hydro constraints are represented in detail, by means of a nonlinear framework. This model presents innovative formulations for hydro units, specifically designed to address pivotal non-differentiable constraints. The proposed multi-period HOPF model is used as a benchmark to assess inaccuracies present in linear or linearized HOPF model variants. The results using the IEEE 24-bus and 300-bus systems, adapted so they become hydropower systems, clearly identify significant impact of some hydro constraints which have been neglected in most models described in the literature, in the hydrothermal operational policy and scheduling.

Techno-economic feasibility analysis of a eucalyptus-based power plant using woodchips

Forest biomass plays a critical role in developing countries, with high potential to contribute to the energy needs as a cheaper and carbon-friendly alternative. In Brazil, eucalyptus plantations have gained attention in recent years as a promising source for the power sector. Aiming to assess this potential, this work analyzed the techno-economic feasibility of a eucalyptus woodchip-based power plant. A typical 10 MW power plant was modeled using empirical parameters based on the Steam Rankine Cycle. Woodchip samples were collected and energy properties, fuel demand, eucalyptus area required and unit variable cost were determined. In economic analysis, NEWAVE (Long and Medium Term Interconnected Hydrothermal Systems Operation Planning Model) and ANAFIN (Financial Analysis Model for Electrical Projects), both developed by CEPEL (Brazilian Electrical Research Center) were used for the first time to analyze forest biomass projects taking into account fuel availability and hydrological risks. Three energy trading scenarios were evaluated: (1) full commercialization in Brazilian regulated market, (2) full in free market and (3) partial in both markets. Results showed that 4,200 ha of eucalyptus plantations would be needed to ensure sustainable woodchip supply. Moisture content, basic density and woodchip price played an essential role in determining future costs. All scenarios of energy trading were economic feasible, and scenario (3) mitigated energy spot price risk and might be considered the effective risk-return strategy. The simulated strategies may improve decision makers visibility over the risk-return relation of a eucalyptus power plant. This methodology may be used to evaluate new forest biomass energy projects in potential areas. This study will help encourage bioenergy and power diversification in developing countries.

Optimal non-anticipative scenarios for nonlinear hydro-thermal power systems

The long-term operation of hydro-thermal power generation systems is modeled by a large-scale stochastic optimization problem that includes nonlinear constraints due to the head computation in hydroelectric plants. We do a detailed development of the problem model and state it by a non-anticipative scenario analysis, leading to a large-scale nonlinear programming problem. This is solved by a filter algorithm with sequential quadratic programming iterations that minimize quadratic Lagrangian approximations using exact hessians in L∞ trust regions. The method is applied to the long-term planning of the Brazilian system, with over 100 hydroelectric and 50 thermoelectric plants, distributed in 5 interconnected subsystems. This problem with 50 synthetically generated inflow scenarios and a horizon of 60 months, amounting to about one million variables and 15000 nonlinear constraints was solved by the filter algorithm in a standard 2016 notebook computer in 10 h of CPU.

Representation of Wind Energy Scenarios in the Mid-Term Hydrothermal Systems Operation Scheduling

This paper presents a modified stochastic dual dynamic programming methodology in order to include the wind energy scenarios in the mid-term hydrothermal systems operation planning with an hourly representation of the demand. As the wind power intermittence adds more complexity for this class of problem, the conventional stochastic dual dynamic programming (SDDP) is not suitable to solve it due the excessive computational effort. Then the proposed approach includes in a conventional SDDP an analytical representation of immediate cost function for each stage and inflow/wind scenarios. Analysis results considering different cases of the South Brazilian system will be used to show the effectiveness and robustness of the proposed methodology.

Discrete Data AGC of Hydrothermal Systems Under Varying Turbine Time Constants Along With the Power System Loading Conditions

The paper deals with the discrete data automatic generation control (AGC) of two area hydrothermal power system operating at different loading conditions. The power system load varies considerably throughout the day and accordingly, thermal and hydropower systems are scheduled to operate at different loading conditions. It is found that steam as well as hydro turbine time constants vary as the power system loading varies. In earlier AGC studies, these parameters are considered to be constant, irrespective of power system loadings. This paper studies the dynamic performance of hydrothermal power system considering the variation of these turbine time constants along with the nominal loading of power system. The studies have been conducted for linear as well as nonlinear models of speed governor and hydroturbine models recommended by the IEEE committee. It is also discovered that conventional empirical formula-based hydraulic governor settings recommended by the IEEE working group are completely unacceptable for hydrothermal power system. A maiden attempt is made to optimize the hydraulic governor settings simultaneously along with the controller gains using metaheuristic algorithm. The comparison of system dynamic responses reveal that optimization-based hydrogovernor settings give the better dynamic performance in case of linear as well as nonlinear models of hydro system, and therefore are strongly recommended over the conventional settings. The paper also deals with the optimum selection of sampling periods for discrete data AGC operation of hydrothermal systems.

Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

Maximizing hydro share in peak demand of power systems long-term operation planning

This paper presents an improvement in the long-term hydrothermal system operation planning by including peak generation as a decision variable in the simulations, in order to better operate the hydro plants. Although this has been widely considered in the short-term, the objective is to correctly set the power system operation planning to avoid instantaneous shortages and blackouts. Thus, the instantaneous generation capacity is included in the objective function in order to better consider this variable in long-term operation planning. The results show that it is possible to significantly increase peak power by simply modifying the operation of hydro plants. Moreover, this approach does not present a significant increase in the operation costs.

Hydrothermal systems operation planning using a discretization of energy interchange between subsystems

This article presents an alternative approach to solve hydrothermal systems operation planning based on stochastic dynamic programming. Under the presented approach, the hydroelectric power plants are grouped into equivalent subsystems of energy and the expected cost functions are modeled by a piecewise linear approximation, by means of the Convex Hull algorithm. Also, under this methodology, the problem is solved independently for each subsystem such that the state variables to be considered are the energy storage and energy net interchange of the subsystem. The presented results have shown that this subsystems separation technique reduces significantly the computation time when compared with the traditional techniques of stochastic dynamic programming, demonstrating the effectiveness of the proposed methodology.

Short-Term Hydrothermal Dispatch With River-Level and Routing Constraints

Recently, there has been a growing need to consider issues related to multiple uses of water-such as navigation, fishing, or recreation-in the operation planning of electrical power systems, specially in predominantly hydro systems. In short-term models, it is of particular importance to consider maximum/minimum values and ramp limits for the stream level of a river. However, in order to properly represent such constraints, it is also necessary to model in detail the river routing, i.e., the water wave propagation along the channels that connect reservoirs in cascade. This paper proposes a linear programming model to represent river stream-level constraints and river-routing effects in the short-term hydrothermal dispatch problem. Results show the high accuracy of the proposed approach to represent maximum hourly and daily river-level variations as well as the impacts of taking into account a detailed representation of river routing in the operation of hydrothermal systems.

Comparison of emerging metaheuristic algorithms for optimal hydrothermal system operation

Optimal hydrothermal system operation (OHSO) is one of the complex and hard-to-solve problems in power system field due to its nonlinear, dynamic, stochastic, non-separable and non-convex nature. Traditionally, this problem has been solved through classical optimization algorithms, which require some approximations to tackle a more tractable variant of the original problem formulation. Metaheuristic optimization has undergone a significant development in recent years, thus, there is a variety of tools with different conceptual differences, which offer a great potential for solving OHSO without extensive simplifications. This paper provides a comparative study on the application of six emerging metaheuristic algorithms to OHSO, namely, the Comprehensive Learning Particle Swarm Optimizer (CLPSO), Genetic algorithm with Multi-Parent Crossover (GA-MPC), Differential Evolution with Adaptive Crossover Operator (DE-ACO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Linearized Biogeography-based Optimization (LBBO), and the Hybrid Median-Variance Mapping Optimization (MVMO-SH). Since these tools have been successfully applied to other hard-to-solve optimization problems, the goal is to ascertain their effectiveness when adapted to tackle the OHSO problem by evaluating their performance in terms of convergence speed, achieved optimum solutions, and computing effort. Numerical experiments, performed on a test system composed by four cascaded hydro plants and an equivalent thermal plant, highlight the relevance of the adopted global search mechanisms, especially for LBBO and MVMO-SH. A nonlinear programming (NLP) algorithm is used as reference to validate the results.

An Efficient Parallel Algorithm for Large Scale Hydrothermal System Operation Planning

This paper presents an efficient parallel algorithm applied to the problem of large scale hydrothermal system operation planning. This problem is solved by stochastic dual dynamic programming. A plan of operation is determined for each stage of the planning period with the objective of minimizing the expected cost of operation over the planning horizon. For each state, the hydrothermal operation problem is modeled as a linear programming problem and the dual variables associated with the solution are used to construct the Benders cuts. The plan of operation is represented by the future cost function, which is approximated by a piecewise linear function, constructed iteratively by the Benders cuts. An optimized parallelization strategy is applied to both the forward and backward cycles of the dynamic programming convergence process with very high parallel efficiency. For a planning horizon of 5 years and 200 inflow scenarios for each reservoir, the sequential solution for the Brazilian system requires almost 15 hours of processing time. The parallel algorithm obtains exactly the same solution in less than 23 min on 64 cores and in less than 17 min on 128 cores.

Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal systems. This model is based on the Periodic Autoregressive Model, PAR (p), where the identification of the p orders is based on the significance of the Partial Autocorrelation Function (PACF) estimated via Bootstrap, an intensive computational technique. The scenarios generated through this new technique were applied to the operation planning of the Brazilian Electrical System (BES), using the previously developed methodology of Stochastic Dynamic Programming based on Convex Hull algorithm (SDP-CHull). The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders models in most cases which retains the statistical characteristics of the original series. Additionally it presents a closer total mean operation cost when compared to the cost obtained via historic series.

Optimization of Large-Scale Hydrothermal System Operation

This paper presents the development of a mathematical model to optimize the management and operation of the Brazilian hydrothermal system. The system consists of a large set of individual hydropowe...

Reference Tracking Controller Applied to the Operation of Hydrothermal Systems

Abstractthis paper considers the long term operation planning of hydrothermal systems modelled by a Markov jump system, using reference tracking controller. The reference is obtained via optimization of a certain cost functional in a deterministic setup, using deterministic dynamic programming. The control is designed employing a linear Markov jump model and quadratic costs. For selecting the quadratic cost weighting matrices, we propose a scheme based on Monte Carlo simulation of the system with the deterministic dynamic programming solution. We present some preliminary case studies suggesting that the reference tracking controller represent an interesting alternative to the deterministic or stochastic dynamic programming solutions.

Minimização do Conflito Entre a Geração de Energia Elétrica e o Controle de Cheias no Planejamento da Operação de Reservatórios I: Teoria

One of the particularities of the Interconnected Brazilian Hydrothermal System operation planning is the need to use part of the volumes of the hydroelectric plants reservoirs as waiting volumes for attenuation of possible floods. These volumes are calculated through the Flood Prevention Studies, based on the Critical Paths of the synthetic flow scenarios. The use of the reservoir volumes for electricity generation and flood control is subject to conflict, since the former attempts to keep the reservoirs as full as possible and the latter to keep them partly empty, for the same period. Thus, the first article, of this series of two, presented three methodological proposals: i) Disregard the waiting volumes allocated for critical paths recession, ii) Use the forecast flows to calculate the critical path and iii) Change the waiting volumes at the end of the rainy season according to the average monthly flow observed in the initial months of the season. In this article, all methodologies proposed were tested in the River Parana basin to check their applicability and effectiveness. For simplification, the River Parana basin was considered as an equivalent reservoir until the reservoir of Jupia, considering just one flood control point downstream of this plant. According to the results, a new methodology is proposed combining two of the methodologies proposed, with better results

Simulation of Optimal Medium-Term Hydro-Thermal System Operation by Grid Computing

This paper presents a model for the simulation of the optimal medium-term operation of a hydrothermal system. System stochastic parameters are modeled by Monte Carlo scenarios, which are solved on distributed processors. For each scenario a yearly hydro-thermal scheduling (HTS) problem with hourly time resolution is formulated and solved as a large mixed integer linear program (MILP). HTS modeling includes unit commitment, start-up costs and minimum up/down time constraints. The model is applied to the Greek power system, comprising 29 thermal units and 13 hydroplants; 100 simulation scenarios are generated and solved on 18 distributed processors. Test results include both medium-term objectives, such as reservoir water management, and short-term decisions such thermal unit start-up decisions.

Optimal reliable operation of hydrothermal power systems with random unit outages

A new model for long-term operation of hydrothermal power systems is introduced and a method for obtaining an optimal solution is also developed. The authors assume that reservoir inflows and energy demand are stochastic and all units are exposed to random outages. The objective is to minimize the total cost of the system as well as the expected interruption cost of energy (EIC) during a given planning horizon. This goal is reached through determination of hydroplant discharges, thermal units energy output, and the system reliability level simultaneously. In fact, the authors integrate long-term hydrothermal system operation planning and system reliability determination in a unified model. Since the resulting model is a large-scale stochastic nonlinear programming, an algorithm is especially developed to solve it. This algorithm that includes decomposition technique, Lagrangian relaxation, and nonlinear and dynamic programming finds an optimal solution within three stages. To test the method, it is implemented for the Khuzestan power system in Iran and the results are analyzed.

Optimal Reliable Operation of Hydrothermal Power Systems with Random Unit Outages

A new model for long-term operation of hydrothermal power systems is introduced and a method for obtaining an optimal solution is also developed. The authors assume that reservoir inflows and energy demand are stochastic and all units are exposed to random outages. The objective is to minimize the total cost of the system as well as the expected interruption cost of energy (EIC) during a given planning horizon. This goal is reached through determination of hydroplant discharges, thermal units energy output, and the system reliability level simultaneously. In fact, the authors integrate long-term hydrothermal system operation planning and system reliability determination in a unified model. Since the resulting model is a large-scale stochastic nonlinear programming, an algorithm is especially developed to solve it. This algorithm that includes decomposition technique, Lagrangian relaxation, and nonlinear and dynamic programming finds an optimal solution within three stages. To test the method, it is implemented for the Khuzestan power system in Iran and the results are analyzed.

Roasting the mantle: Slab melting and the genesis of major Au and Au-rich Cu deposits

The generation of large deposits of Au and Cu in suprasubduction-zone settings depends upon a combination of factors, including availability of the chalcophile elements to arc magmas in their mantle source regions, and the operation of suitable hydrothermal systems in the upper crust where the deposits eventually form. The removal of chalcophile elements from the mantle wedge into arc magmas can only occur if sulfide is absent from the melted source rock, requiring oxidation of the mantle wedge to values of log f O 2 > FMQ + 2, where f O 2 is oxygen fugacity and FMQ is the fayalite-magnetite-quartz oxygen buffer. The only agent capable of effecting this change is ferric iron, carried in solution by slab-derived partial melts or supercritical fluids. Arc magmas with high potential to generate Au and Cu deposits will have certain geochemical characteristics; they will have f O 2 more than two log units above FMQ and they will have either adakitic, sodic-alkaline, or potassic-ultrapotassic affinities. Favorable tectonic settings include subduction of very young lithosphere or very slow or oblique convergence, flat subduction, and the cessation of subduction.

Optimal operation of large hydrothermal systems applying parallelization techniques

Different alternatives to instrument parallelization techniques of long-term hydrothermal scheduling algorithms are given. First, a model for long-term operation planning is outlined, where uncertainties are handled with probabilistic and simulation techniques, and where hydrothermal optimization is carried out using dynamic programming (DP). Then, the application of parallelization to stochastic calculation is given, followed by four ways to solve DP in parallel. Finally, a cross-comparison is made regarding performance and code complexity using a test case, focusing on run-time characteristics of parallel DP algorithms.