Application of chaos assisted sine cosine algorithm on wind–solar integrated hydrothermal scheduling problem

In recent times, renewable energy sources are essential for generating low-cost, pollution-free power. Since the last two decades, hydrothermal coordination system has been used to fulfill the demand power. The traditional generating model's two biggest flaws are high generation cost and excessive fossil fuel emission. In the proposed work, generation process of traditional hydrothermal scheduling (HTS) has been improved by integrating wind–solar energy. To overcome current issues of HTS, that is, excessive emission and generation cost, hydrothermal–solar–wind scheduling (HTSWS) problem has been introduced where two wind and two solar power generating units have been considered with four hydro and three thermal power generating units. Here, effective performance of low cost-emission based proposed HTSWS has been examined by using chaos-based sine cosine algorithm (SCA) (CSCA). Chaos has been used in sine cosine function of SCA to improve searching process for finding global optimal results. The proposed CSCA has been used to investigate three separate situations of standard HTS and recommended HTSWS problems including generation cost, emission, and combined cost-emission. The cost of generation and emissions from conventional HTS can be reduced by 11% and 68%, respectively, by the suggested renewable energy source-based generation model. The results of several case studies of proposed HTSWS and standard HTS reveal that wind–solar energy is effective in the HTS issue, and chaos assists in the SCA to improve its optimization process. In this study, effectiveness and robustness of the CSCA have been demonstrated by comparing the CSCA-based results to existing methods for the challenges under consideration.     

Hybrid cross entropy—cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads

The demand of energy is increasing due to the growing population of the world and improvements of technology. One of the best significant solution techniques to fulfill this energy demand is utilization of renewable energy sources (RESs). Modern power systems, which integrate RESs, such as wind, small hydro or solar energy sources need to carry out the uncertainty by the accessibility of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow (OPF) problems. This paper proposed a novel hybrid meta-heuristic algorithm entitled cross entropy—cuckoo search algorithm (CE-CSA). The application of levy flights in the cuckoo search algorithm (CSA) improves the local exploitation capability while the CE method is used in the initial stage for global exploration due to its fast convergence. The effectiveness of the proposed hybrid algorithm has been demonstrated in solving the OPF problem, considering RESs and controllable loads for different stochastic scenarios in a benchmark system to minimize the total operation cost. To verify its effectiveness, its performance is compared with the most advanced and recently proposed hybrid meta-heuristic techniques. Simulation results show that the proposed algorithm can solve the OPF problems with RESs and controllable loads efficiently and can give better solutions compared to different techniques. The conventional statistical method called analysis of variance (ANOVA) test, T ukey honestly significant difference test, and Wilcoxon sign rank test are performed for comparative analysis of different techniques. The results of this test show the validation of CE-CSA compared to different optimization techniques.     

Study of wind-solar based combined heat and power economic dispatch problem using quasi-oppositional-based whale optimization technique

Source of fossil fuel is impoverishing in the upcoming future. Renewable energy sources (RESs) are becoming challenging conventional energy substitutes in the present scenario. In this article, an attempt has been made to utilize RESs such as wind and solar energy with combined heat and power economic dispatch problems. The intention of this presentation is to minimize conflict objectives such as fuel cost accomplished with load demand along with transmission losses while satisfying all the constraints. A new optimization technique, namely a quasi-oppositionalbased whale optimization algorithm (QOWOA) is adopted to cope up with the non-linearities of the chosen systems. The proposed technique is tested on two different nonlinear realistic power systems to achieve the satisfactory performances. The superiority of the proposed QOWOA algorithm is judged by comparing it with some recently developed metaheuristic optimization techniques.     

Renewable energy management of an hybrid water pumping system (photovoltaic/wind/battery) based on Takagi–Sugeno fuzzy model

This research work presents an optimal energy management for a hybrid water pumping system driven by a photovoltaic generator (PVG) and a wind turbine. These two renewable energies are used as power generation sources, whereas a battery is added as an energy-storing system, for the purpose of controlling the power flow and providing a constant load supply. The proposed management system, serve to guarantee the pumping system autonomy in a rural region where's no access to the electrical network. As a result, a maximum power point tracking (MPPT) controller is created based on the fuzzy Takagi–Sugeno (TS) model, ensuring maximum power transfer to the moto-pump in spite of wind speed and insolation changes. The synthesis of MPPT control law involves TS fuzzy reference models which generate the desired trajectories to track. A supervisor has been developed for energy management and its major purpose is to effectively use the battery to satisfy the power load requirements, and that is by maintaining the state of charge (SOC) to extend the battery's life. Finally, simulation results have been done based on Matlab/Simulink with the aim of validating the efficiency of the proposed energy management supervisor.     

Performance of different optimal charging schemes in a solar charging station using dynamic programming

Electric Vehicles (EVs) are gradually replacing conventional vehicles as they are environmentally friendly and cause less pollution problems. Unregulated charging has severe impacts on the distribution grid and may incur EV owners higher charging costs. Therefore, controlled charging infrastructures to supply the charging needs of large numbers of EVs are of vital importance. In this article, an optimal control scenario is presented to formulate the charge scheduling problem of EVs in a solar charging station (CS). Two different objective functions are considered. The first objective function holds for minimizing the total charging cost of EVs. In this case, the benefits of Vehicle-to-Grid (V2G) are investigated by comparing the charging costs of EVs with and without this capability. The total EV charging costs and grid benefits are also investigated in the second objective function which holds for minimizing the extracted power from the grid. A modified version of Dynamic Programming is used to solve the large state-space model defined for the optimal control problem with extremely shorter computation time and minimal loss of optimality. Extensive simulations are done in two representative summer and winter climates to determine the role of solar energy in the CS performance. The results show that in the cost minimization algorithms, significant savings for EV owners and a smooth load shape for the grid are achieved. For the minimized power from the grid algorithm, a total near Photovoltaic (PV)-curve charging power is obtained to exploit the PV power as much as possible to minimize the impacts on the grid.     

Analysis of moth flame optimization optimized cascade proportional-integral-proportional-derivative controller with filter for automatic generation control system incorporating solar thermal power plant

In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional-integral (PI)-proportional-derivative (PD) controller with filter (PI-PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two-area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO-tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI-PDF controller performs better than PI controller does. Then, the study is extended to a three-area interconnected hybrid system with proper generation rate constraint. Area-1 consists of solar thermal-thermal unit; area-2 consists of thermal-hydro unit and area-3 thermal-gas unit as generating sources. Performance of PI-PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI-PD controller without filter. Result analysis divulges that MFO-tuned PI-PDF controller performs better than all other controllers considered in this article. Robustness of the PI-PDF controller is evaluated using parameter variations and random load variation.     

Hybrid BOA-GWO-PSO algorithm for mitigation of congestion by optimal reactive power management

The generation of power with load optimization, particularly in the current deregulated electricity market conditions, is a very important process for improved planning and operation of the grid. In addition, it is very important for the system not to experience problems due to congestion, have tensile stability, and protection to increase the share of electricity from renewable sources with the current supply system. This article presents load balancing with the butterfly optimization algorithm (BOA) in a hybridized form to minimize and maximize loads when used in pool and hybrid markets. The methods have been designed to prevent the drawbacks of BOA and generate a better trade-off between exploration and exploitation abilities by hybridizing it with particle swarm optimization (PSO) and gray wolf optimizer (GWO). Empirical research on other algorithms shows that proposed hybrid BOA-GWO-PSO algorithm performs better and shows potential in diverse problems. These studies give it a significant advantage over BOA in general, and when it is employed to solve complex optimization problems validated on benchmark IEEE 30 bus system. A comparative analysis has been conducted to validate the potency of the hybrid BOA-GWO-PSO approach with some conventional meta-heuristic algorithms. Analysis of results by mathematical validation on 23 benchmark functions and application in congestion management by optimal reactive power management (RPM) reveal that the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state-of- art literature.     

A binary-regularization-based model predictive control applied to generation scheduling in concentrating solar power plants

This paper proposes the use of model predictive control (MPC) with binary-regularization to manage the electric power generation problem in concentrating solar power plants with thermal energy storage. The main advantage of the of MPC with binary-regularization formulation is the inclusion of a power block protection method based on a binary-regularization term that penalizes power generation variation (also called generation cycling) differently according to the power block situation, ie, normal operation, startup, or shutdown. This distinction simplifies the choice of schedules with reduced variation and high energy sale profits. The interest in this reduction is the achievement of a higher lifetime of the power block elements, lower maintenance costs, and easier plant operability. A benefit of the generation scheduling based on MPC is the capacity of rescheduling the power generation at regular periods, taking advantage of the most recent energy prices and weather forecast, and of the plant's current state. An interesting question is if the proposed protection mechanism affects the economic results of the MPC black strategy. In this regard, an economic study based on a realistic simulation of a 50 MW parabolic trough collector-based concentrating solar power plant with thermal energy storage, under the assumption of participation in the Spanish day-ahead energy market scenario, is included. Realistic values for actual and forecasted solar resource and for energy price are used, and for penalties for deviation from the committed generation schedule. The economic study shows that the proposed scheduling method provides an important reduction of the generation cycling without decreasing energy sales profits. Another advantage of the proposed method is the possibility of estimating the highest level of power block protection, which maintains the profits by means of historical data, which favors its practical implementation.     

Optimal active-reactive hydro-thermal schedules using functional analysis

A new approach to the hydro-thermal optimal active and reactive power scheduling problem is proposed. The optimal schedules for a system with thermal and common-flow hydro-plants are obtained. The active-reactive power balance model of the electric network is introduced. Although not as accurate as the load-flow model, the present model is simpler. The scheduling problem is solved by use of the minimum norm formulation of functional analytic optimization.     

Sequential quadratic programming and analytic hierarchy process for nonlinear multiobjective optimization of a hydropower network

In this paper, an optimal annual scheduling for power generation (i.e. rule curves and volume of water releases) in serial or parallel hydropower plants is developed. Multiobjective programming and weighted sum method are used to convert a multiobjective problem to a single objective one. Furthermore, to obtain viable alternatives under existing uncertainty, some random weight vectors in the whole weighting space are generated and for each weight vector an optimal solution is found using sequential quadratic programming (SQP). Then, analytic hierarchy process (AHP) is used to select the best solution according to a given criterion, and determine the most preferred one. Besides, this method does not require choosing a priori preference for the objective function, thus making an ideal tool for handling complicated multiobjective models and easy to use with the aid of computer programs. Combination of multiobjective optimization and multicriteria decision analysis (MCDA) is an integrated methodology that is capable of dealing with complex water management problems. Various scenarios for dry, median, and wet years are assumed based on stochastic flows from external sources (e.g. flooding rivers, unexpected rains, etc.) coming into each reservoir, and turbine power generation is obtained from hill diagrams provided by the manufacturer. The application of this methodology is illustrated in a case study for optimal scheduling of Karoon River Basin, where the decision support system and optimization routines are implemented in MATLAB. The total energy productions for 10 optimized solutions under dry, median, and wet scenarios (generated from forty‐year historical inflow records) are calculated, and specific water consumption for each reservoir is obtained in different months. This can significantly help decision makers to have an optimal and more intelligent management over energy productions in hydropower plants and associated thermal power plants. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimal control of a wind energy conversion system and a wind turbine

Currently, a maximum power point tracking (MPPT) unit implemented in a wind energy conversion system (WECS) extracts the maximum mechanical power from the wind turbine used in the WECS. Therefore, the MPPT unit acts as a maximum mechanical power tracker (MMPT) that performs optimal control of the wind turbine to extract the maximum mechanical power from the wind turbine. In this paper, the basic concept of a maximum electrical power tracker (MEPT) is presented both theoretically and technically. It is demonstrated that an MEPT implemented in a WECS maximizes the output electrical power of the WECS. Thus, in contrast with an MMPT, the proposed MEPT optimally controls the whole of the WECS, rather than the wind turbine, to extract the maximum electrical power from the WECS. Since, in the WECS, the power efficiency of the whole of the WECS, not the wind turbine, should be maximized to extract the maximum output electrical power from the WECS, the conventional MPPT unit acting as an MMPT should be replaced with the proposed MEPT, and this is the superiority of the proposed MEPT to an MMPT or a conventional MPPT unit. To provide experimental verifications, 2 novel MEPT and MMPT with simple structures and better performance compared to the MPPT techniques commonly used in WECSs have been constructed, which are presented in detail.     

Development of MADB of P-I controller using LMI technique in a renewable energy based AGC system and study its application in a deregulated environment including energy storage device

In recent decades, inclusion of renewable resources is considered as one of the most promising options for the long run uninterrupted power supply without depending on conventional resources. Thus, the renewable energy generation will get more attention and massive growing, so that the goal of 40% share of electricity in the worldwide energy portfolio in 2050 would be realized. But during replacement of renewable energy by conventional energy, engineers are facing a lot of problem due to solar generation, wind generation change their characteristic rapidly with weather condition, which may cause large synchronizing imbalance between different units and generate large system delay or communication delay in large interconnected grid. In this article, the authors propose linear matrix inequalities techniques to developed margin of allowable delay for delay dependent stable hybrid system. Initially, to judge the efficacy of proposed chaotic atomic search optimization (CASO) algorithm over other evolutionary algorithms with PID controller, a thermal-hydro gas system is considered. The second part of this article is motivated by the fact that proposed CASO algorithm with P-I controller is superior in contrast to bacterial foraging algorithm technique. In addition to this, some energy storage devices such as fuel cell, aqua electrolyzer, and ultra-capacitor are used to achieve a better dynamic response within specified delay margin. Moreover, to study the impact of communication delays (delay margin) due to the loss of synchronism between solar-wind (for their unpredicted environmental features) and thermal unit (nonlinearities like generation rate constraints, boiler dynamics, and governor with the dead band) are considered in thermal unit and the simulation results verify with the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performances is evaluated under deregulated environment. The simulation results help to make an inter-relation between the delay margin and the controller gain (P-I) which help the system operator in designing controllers gain for stable operation of the proposed hybrid system.     

Minimum fuel powered dynamic soaring of unmanned aerial vehicles utilizing wind gradients

This paper studies optimal powered dynamic soaring flights of unmanned aerial vehicles (UAVs) that utilize low‐altitude wind gradients for reducing fuel consumptions. Three‐dimensional point‐mass UAV equations of motion are used, and linear wind gradients are assumed. Fundamental UAV performance parameters are identified through the normalization of the equations of motion. In particular, a single wind condition parameter is defined that represents the combined effect of air density, UAV wing loading, and wind gradient slope on UAV flight. An optimal control problem is first used to determine bounds on wind conditions over which optimal powered dynamic soaring is meaningful. Then, powered UAV dynamic soaring flights through wind gradients are formulated as non‐linear optimal control problems. For a jet‐engined UAV, performance indices are selected to minimize the average thrust required per cycle of powered dynamic soaring that employs either variable or constant thrust. For a propeller‐driven UAV, in comparison, performance indices are selected to minimize the average power required per cycle of powered dynamic soaring with either variable or constant power. All problem formulations are subject to UAV equations of motion, UAV operational constraints, proper initial conditions, and terminal conditions that enforce a periodic flight. These optimal control problems are converted into parameter optimization with a collocation method and solved numerically using the parameter optimization software NPSOL. Analytical gradient expressions are derived for the numerical solution process. Extensive numerical solutions are obtained for a wide range of wind conditions and UAV performance parameters. Results reveal basic features of powered dynamic soaring flights through linear wind gradients. Copyright © 2004 John Wiley & Sons, Ltd.     

Optimal control of electrical vehicle incorporated hybrid power system with second order fractional-active disturbance rejection controller

This article proposes a novel control methodology employing a fractional-active-disturbance-rejection-controller for the combined operation of load frequency control and automatic voltage regulator of a hybrid power system. A two area hybrid power system with diverse energy sources like solar-thermal, conventional-thermal and wind sources equipped with appropriate system nonlinearities is investigated. In order to ascertain the role of modern-day electric-vehicle (EV), the hybrid power system is incorporated with EVs in both the areas. To establish an effective frequency, voltage and tie line power control of the hybrid power system, a second order fractional-active-disturbance-rejection-controller with fractional-extended state observer is modeled as secondary controller. Magnetotactic-bacteria-optimization (MBO) technique is applied to obtain optimal values of the controller gains and the hybrid system parameters. The robustness of the controller gains is tested under different system parameter changes from their nominal values. In addition, the effect of incorporating a power system stabilizer on the hybrid power system is evaluated. Further, the impact of integrating renewable sources and EVs in the hybrid power system is explored. Moreover, the stability of the hybrid power system is monitored with the inclusion of FACTS device. The developed controller operates encouragingly with reference to system stability, rapidity and accuracy in comparison to testified control strategies available in the literature. The robustness test under load-perturbation, solar-insolation, wind input variations also proves the efficiency of MBO optimized second order fractional-active-disturbance-rejection-controller gains.     

Optimal decentralized control of a wind turbine and diesel generator system

This article presents a decentralized optimal controller design technique for the frequency and power control of a coupled wind turbine and diesel generator. The decentralized controller consists of two proportional-integral (PI)-lead controllers which are designed and optimized simultaneously using a quasi-Newton based optimization technique, namely, Davidon–Fletcher–Powell algorithm. The optimal PI-lead controllers are designed in such a way that there are no communication links between them. Simulation results show the superior performance of the proposed controller with a lower order structure compared to the benchmark decentralized linear-quadratic Gaussian integral controllers of orders 4 and 11. It is also shown that the proposed controller demonstrates an effective performance in damping the disturbances from load and wind power, as well as a robust performance against the parameter changes of the power system.     

Comparative performance analysis of 2DOF state feedback controller for automatic generation control using whale optimization algorithm

This paper presents the design of two‐degree‐of‐freedom state feedback controller (2DOFSFC) for automatic generation control problem. A recently developed new metaheuristic algorithm called whale optimization algorithm is employed to optimize the parameters of 2DOFSFC. The proposed 2DOFSFC is analyzed for a two‐area interconnected thermal power system including governor dead band nonlinearity and further extended to multiunit hydrothermal power system. The supremacy of the 2DOFSFC is established comparing with proportional‐integral, proportional‐integral‐derivative (PID), and 2DOFPID controllers optimized with different competitive algorithms for the concerned system. The sensitivity analysis of the optimal 2DOFSFC is performed with uncertainty condition made by varying bias coefficient B and regulation R parameters. Furthermore, the proposed controller is also verified against random load variations and step load perturbation at different locations of the system.     

Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling

Using hydrogen (H) and ammonia (NH) for renewable energy storage has the potential to enable economical power and heat supply with high renewable penetrations, especially in remote locations which are characterized by high energy costs. In this work we assess the economic competitiveness of renewable combined heat and power (CHP) systems in Mahaka HI, Nantucket MA, and Northwest Arctic Borough (NWAB) AK by optimally designing these systems for scenarios in which power and heat can be purchased over a range of historical energy prices as well as when 100% renewable supply is required. We use a combined optimal design and scheduling model which minimizes annualized net present cost by determining optimal technology selection and size simultaneously with optimal schedules for each period of a system operating horizon aggregated from full year hourly resolution data via a consecutive temporal clustering algorithm. We find that renewable generation meets at least 85% of power demands and 75% of heat demands under the lowest energy prices investigated. Higher conventional energy prices lead to increased renewable penetration which is facilitated by renewable NH as a seasonal energy storage medium, as are 100% renewable CHP systems. NH is used for power generation with heat cogeneration in all three locations, as well as directly for heating in NWAB. On an annual cost basis, NH-enabled 100% renewable CHP is only 3% more expensive in Mahaka and NWAB than systems which can purchase energy at the lowest prices, while it is 15% more expensive in Nantucket.     

Disturbance observer–based dynamic optimal setting control scheme for processes with nonlinear loops

This paper is concerned with optimal operational control problems that exist in industries. The performance index is optimized by set points reselection on the operational control layer together with controllers design on the loop control layer. Firstly, the operational indices need to be obtained through some optimization algorithms. Secondly, the widely used PID controllers are adopted to achieve performance optimization in the ideal situation. To minimize performance deterioration caused by harmonic disturbances, the disturbance observer–based optimal setting control is proposed for those industrial processes with nonlinear loops. In the proposed method, the controller structure or parameter is never changed, which is accordant with actual industrial conditions. Finally, numerical simulations are given to demonstrate the effectiveness and convenience of the results.     

Experimental investigation of Harris Hawk optimization-based maximum power point tracking algorithm for photovoltaic system under partial shading conditions

An integrated quasi Z-source DC–DC converter (qZSC) along with Harris Hawk Optimization (HHO)-based maximum power point tracking (MPPT) algorithm is proposed in this paper to increase the efficiency of photovoltaic (PV) system. The qZSC-based PV system experiences more voltage and current stress during partial shading conditions (PSCs), which causes overheat on qZSC components hence, degrade the efficiency and reliability of the system. Conventional swarm intelligence-based MPPT algorithms track the G_MPP during PSC, but these take longer convergence time and fail to settle at G_MPP. This uncertainty of finding the G_MPP leads to fluctuations at output of qZSC, hence more stress on the converter components. HHO in tracking the G_mpp eliminates premature local MPPs, enhances convergence speed by expanding the search space for finding the G_MPP. The proposed system is developed in MATLAB/Simulink environment and verified the results by developing prototype model in the laboratory by using C2000™ Piccolo™ Launch Pad™, LAUNCHXL-F28027 controller. The tracking performance of the proposed HHO-based MPPT algorithm is tested under fast changing and PSCs in comparison with perturb & observe (P&O), particle swarm optimization (PSO), and artificial bee colony (ABC)-based MPPT algorithms. The simulation and experimental results show that the proposed HHO-based MPPT algorithm is robust, tracks maximum power point in minimum convergence time in comparison with P&O, PSO and ABC-based MPPT algorithms. Hence, voltage and current fluctuations at the output of qZSC are reduced. Therefore, voltages and current stress on qZSC components are reduced and the efficiency of the system is improved.     

Building-to-grid optimal control of integrated MicroCSP and building HVAC system for optimal demand response services

The world is shifting toward cleaner and more sustainable power generation to face the challenges of climate change. Renewable energy sources such as solar, wind, hydraulic are now the go-to technologies for the new power generation system. However, these sources are highly intermittent and introduce uncertainty to the power grid which affects its frequency and voltage and could jeopardize its stable operations. The integration of micro-scale concentrated solar power (MicroCSP) and thermal energy storage with the heating, ventilation, and air conditioning (HVAC) system gives the building greater leeway to control its loads which can allow it to support the power grid by providing demand response (DR) services. Indeed, the optimal control of the power flowing between the MicroCSP, the HVAC system, and the thermal zones can bring additional degrees of freedom to the building which can be relegated to the power grid based on the objective function and the incentives provided by the latter. This article presents an in-depth investigation of the MicroCSP potential to provide ancillary services to the power grid. It focuses on evaluating the effect of incentives provided by the power grid on the building participation to the load following programs. It also demonstrates how the MicroCSP can help the building deal with constraints related to load peak shaving and ramp-rate reduction set by the power grid as part of long-term DR contracts. A sensitivity analysis is carried out to confront the results to prediction uncertainties of the energy prices and the weather conditions.