Multiobjective optimization for hydro‐photovoltaic hybrid power system considering both energy generation and energy consumption

Abstract

Hydropower can be an ideal compensation for fluctuant photovoltaic (PV) power due to its flexibility. In this study, a multiobjective optimization model considering energy generation and consumption simultaneously for a hydro‐PV hybrid power system is proposed. With the daily mean radiation intensity and temperature, the PV power output is calculated. Taking reservoir release as the decision variable, the total energy generation of the hydro‐PV system is maximized. Meanwhile, the gap between the energy generation and the energy consumption is minimized to reduce the abandoned PV power or hydropower. The proposed multiobjective model is optimized by Non‐dominated Sorting Genetic Algorithms‐II (NSGA‐II). The Longyangxia Project, the largest hydro‐photovoltaic hybrid power system in the world is taken as the study case to estimate the optimal operational strategies for different objectives in wet year, normal year, and dry year, respectively. The optimal operation process of the reservoir is presented, which is instructive for the operation in the future.

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

“…Optimization can be defined as the process to determine the solution which provides the minimum value of a problem that numerically present relevant value of stakeholders in the system . An optimization problem is usually expressed as a vector minimization:where x is an n ‐dimensional decision variable, is the feasible solution space, f ( X ) is the objective function with n objectives, and contains m relevant constraints. To solve such an optimization problem involves multiple objectives, corresponding algorithms are necessary.…”

“…Among the conducted literature, various optimization techniques have been investigated to achieve optimal solutions of hybrid energy systems . Among new optimization methods, particle swarm optimization (PSO) provides an efficient technique to address optimization of hybrid systems and has been shown to efficiently solve multidimensional functions with less computational effort and higher efficiency in various fields .…”

“…This paper constructs an optimization model of WF‐HESS with multiple objectives, so multi‐objective particle swarm optimization (MOPSO) algorithm is used to obtain the solutions. Usually, the optimal solutions of a multi‐objective problem is a solution set instead of a single one, namely Pareto‐optimal solutions or Pareto Front . In this context, it's difficult to select the best solution based on the Pareto Front obtained since it requires high accuracy and long processing time .…”

Bayesian network approach to fault diagnosis of a hydroelectric generation system

“…4 In Brazil, 375 small hydropower plants with the total capacity of 4799 MW are currently running, and another 1701 MW installed capacity will be constructed in the next 10 years. Obviously, the economic benefit and carbon dioxide mitigation of such hydroelectric generating systems are well known to the general public, [6][7][8][9][10][11] but the stability and safety impacts of themselves still require enough attentions. Obviously, the economic benefit and carbon dioxide mitigation of such hydroelectric generating systems are well known to the general public, [6][7][8][9][10][11] but the stability and safety impacts of themselves still require enough attentions.…”

“…5 Hydroelectric generation systems are under construction all over the world to ensure the enforcement of stricter energy and environmental policy. Obviously, the economic benefit and carbon dioxide mitigation of such hydroelectric generating systems are well known to the general public, [6][7][8][9][10][11] but the stability and safety impacts of themselves still require enough attentions.…”