An overview on optimal control of renewable resources, methods and challenges

Due to the negative effects of fossil fuel consumption on the environment and the need to reduce emissions in the world, the use of renewable sources, which are widely available, cost-effective and long-lasting, has been increased worldwide. Most of Renewable energy sources (for example wind and solar energy) are alternating and oscillate according to the weather conditions. Therefore, the uncertainty of energy generation by them threatens the security and reliability of the electrical power grid, and the control of the renewable resources is essential in order to improve the power grid performance. Optimal control of renewable resources enables the power grid to respond to load demand adequately at any time and it increases system reliability. The purpose of this study is to review the existing methods for optimal control of these resources and discuss the challenges that exist to control these resources in the field of sustainable development. This paper reviews the latest research in this field with the aim of helping to achieve sustainable development and smart cities and is a useful source for researchers and energy strategy planners.


Introduction
Due to the environmental and economic problems of fossil fuel consumption, efforts to provide more sustainable energy have increased, renewable energy sources are used in power plants, and electricity generation from renewable energy has grown exponentially around the world [1]. Renewable energy is a key solution to the global climate challenge and has benefits such as reduced energy costs, environmental benefits, health benefits, and macroeconomic benefits [2][3][4]. In general, renewable energy sources in a power system reduce dependence on fossil fuel consumption, improve voltage characteristics and increase power system reliability [5].
High penetration of renewable sources leads to challenges of frequency and voltage stability. The most important technical issue is the difficulty of achieving frequency stability of these systems. In addition, new power systems have little inertia [6]. Therefore, the use of control and optimal control techniques for these systems is essential [7].
Existing methods in the field of renewable resource control are divided into two categories: normal control and optimal control, and the results show that optimal control has a better performance to increase network efficiency than conventional resource control. Optimal control is an extension of change calculus and a mathematical optimization method for obtaining control rules. Optimal control is a set of differential equations that describes the path of control variables and optimizes the objective function [8].
Researchers have done extensive research on the control of renewable resources and their relationship to achieving sustainable development, for example in [9] the author describes the relationship between renewable energy and sustainable development with reference to practical cases. In [10], the perspective of renewable energy in developing strategies for sustainable development is discussed. In [11], reviews the role and challenges of renewable resources in the field of sustainable development. In [12], energy management and control system with a large volume of renewable energy sources is introduced. Maintaining the frequency stability of renewable sources in low inertia microgrids is a serious challenge. In reference [13], the appropriate value of the inertial constant is adjusted along with the frequency drop coefficient of the distributed energy sources and the load frequency coefficient to improve the frequency stability. In [14] the use of renewable energy in Africa (and Turkey [15]) has been studied as case studies and various energy policies have been analyzed and the challenges of optimal control of renewable resources from the perspective of Sustainable development are addressed. In [16], offers a new way to control the voltagefrequency of renewable energy sources. In the reference [17], a control method of the power system interface for injecting renewable energy sources into the grid are developed based on the least-squares recursive-multiple output algorithm and are presented taking into account the active power management considerations.
In [18], charge frequency control (LFC) is used for renewable energy in a microgrid. Recently, advanced controllers such as fuzzy logic [19], artificial neural networks [20], and model predictive control [21] have been used in renewable systems and intelligent networks. In [22], the model predictive control is used for the optimal mode of converter switching in renewable systems and microgrids. In reference [23], the limited model predictive control for wave energy converters (WECs) is used. The proposed control strategy uses a complex nonlinear wave model and optimal control in the form of switching functions generates maximum electrical energy. In [11,24,25] Express the challenges of renewable resources in the field of sustainable development, but do not address the solutions of renewable resources to address the challenges.
This paper describes the methods of optimal control and control of renewable resources and also examines the challenges of sustainable control. Sustainable development and achieving it is a new concern of developing countries, and to achieve it, it is necessary to use and control renewable resources.

Material and Method
To control renewable resources, several methods are referred to in the table 1. Interactive control For the renewable unit, it does not allow frequency setting because it cannot control the initial motion (wind and the sun). Frequency stability improves the benefits of each source. Easy integration of renewable energy . [26] MPC (model predictive control) Optimization of power plant output to deal with renewable energy error. Check the operational plan in four seasons . [27,28] Cooperative control Suitable for overcoming limitations such as steady state frequency and improper amount of power. The cooperative control method is able to communicate between all existing components of the network, in addition, it has an extensive central controller for analyzing the data collected from the components. [29] intelligent control It has fast dynamics and does not require system model information but is complex. [30,31] Multi-channel control system in the industry chain For renewable resources, it can be designed and constructed. Due to the multi-channel structure, the large system of renewable resources chains decompose into three subsystems. [32] Slider mode controller Dynamic planning method uses. [33] Lyapunov-based control Is in the form of A-B-C. [34] Inertia-based controller ꭃ Reducing the effect of wind and sun fluctuations in reducing load disorders, adjustment of function reinforcement frequency [35] Slider control (SMC) for waveproducing power plants Energy increases the extraction of force and improves the system and is against disruption in the system. [36,37] Control of three-phase voltage source converter (two-ring system structure or LCL filter) The control design for the LCL converter is much more complicated than the L-type converter because the number is more equivalent. [38] Voltage regulation control in two time intervals (VCC) It is analyzed in two time intervals of 15 minutes. Used to quickly neutralize voltage fluctuations. [39] Use the BESS battery to compensate for the power of the wind farm / PV system It does not require a converter and has less computation time than other methods. [40] Using Bess Battery to Compensate Wind Farm Power / PV There is no need to provide math converter and less calculation time than other methods . [38]

Results and Discussion
This article examines renewable resource control strategies and outlines the advantages and disadvantages of the methods. Using simple PI / PR controllers simultaneously or continuously is the most common method. A PID controller and a fuzzy logic controller were compared, and the results show that the performance of both controllers is similar, but since the PID controller is easier to implement, it has been selected as the best option. In addition, predictive control of the model is faster in terms of load and frequency control.
With the increase in the number of microgrids and the use of renewable energy sources, the complexity and non-linearity of power systems increases and causes that conventional and inflexible controllers do not show proper performance in a wide range of work points. Artificial neural networks have been used as one of the most powerful tools in systems optimization and intelligent processes to automatically adjust and optimize the coefficients of a classical proportionalintegral (PI) controller. Optimal control is a powerful control method that provides economical use of load resources. The results show that the optimal controller is better than the conventional ACE control policy. One of the optimal design methods is H∞ / H.

Conclusions
Renewable energy sources are an important tool to reduce reliance on conventional fuels. However, some renewable energy sources, such as wind and solar, are intermittent and their uncertainty threatens the operational security of the electricity grid. To solve this problem, optimal resource control solutions are proposed. Our aim in this paper is to review the existing methods for controlling renewable energy in the field of sustainable development because energy is a prerequisite for development and sustainable and renewable energy systems are a prerequisite for achieving sustainable development.
In addition, in this article, the advantages and disadvantages of each of the methods of optimal control of renewable resources are identified and because the output of renewable resources is not controllable, the challenges in this field are described in detail and finally the results are examined. Consequences of the transition to sustainable energy supply will make the operation of a power system traditionally and reliably difficult in the traditional way. In particular, balancing the system will be difficult, and changes in flow direction can pose challenges. Financing risks play a much bigger role for renewable energy than fossil fuel energy. In addition to higher costs for solar and wind energy, all renewable energy sources, including hydropower, have a different investment source from fossil fuel-based energy sources. They require large investments first and then lower costs. Therefore, there is a great need for budget, so to achieve sustainable development requires cost and use of optimal control methods, and the uncontrollability of renewable resources has created challenges in the field with approaches such as meteorological forecasts to The control of these resources can be improved to some extent. Researchers interested in research in this field can study the methods of optimal control of renewable resources that are done using neural networks and address the existing challenges to achieve sustainable development. Also, the integration of methods in smart grids can significantly help to transfer a sustainable energy source.

Abstract
Due to the negative effects of fossil fuel consumption on the environment and the need to reduce emissions in the world, the use of renewable sources, which are widely available, cost-effective and long-lasting, has been increased worldwide. Most of Renewable energy sources (for example wind and solar energy) are alternating and oscillate according to the weather conditions. Therefore, the uncertainty of energy generation by them threatens the security and reliability of the electrical power grid, and the control of the renewable resources is essential in order to improve the power grid performance. Optimal control of renewable resources enables the power grid to respond to load demand adequately at any time and it increases system reliability. The purpose of this study is to review the existing methods for optimal control of these resources and discuss the challenges that exist to control these resources in the field of sustainable development. This paper reviews the latest research in this field with the aim of helping to achieve sustainable development and smart cities and is a useful source for researchers and energy strategy planners.