The growing demand for energy is driven mainly by population and income growth. As projected by the United Nations, the human population is projected to reach 9.7 billion in 2050 (vs. 8.0 billion in December 2022), implying that in 2050 there will be an additional 1.7 billion energy consumers. Concurrently, under Wood Mackenzie's base-case outlook, the global economy is forecast to double in size—from US$85.6 trillion in 2022 to US$169.0 trillion—by 2050. These changes translate into an increase of nearly 40% in the world energy consumption over the next 28 years, according to the U.S. Energy Information Administration. This increasing demand for energy and the growing awareness of the severe environmental impacts of using fossil fuels have created significant interest in optimal energy generation from renewable and reusable energy resources, and in the use of more efficient energy-converting and energy-consuming systems. Great interest in improving the efficiency of energy conversion, storage, distribution, management, and consumption systems has created a plethora of optimization and optimal control problems. For example, the highly intermittent nature of direct renewable energy resources (such as sunlight and wind) requires optimal integration of these power generating systems with power storage systems and power grids, so that the integration does not lead to degradation of the quality of power that the grids deliver. In the past two decades, major advances have been made in optimal design and operation of energy conversion, storage, distribution, management, and consumption systems, and optimal integration of these systems, via applying optimization methods. This special issue includes articles that report studies on optimal design and operation of these systems and their optimal integration. These studies deal with systems such as photovoltaic (PV) systems, concentrated solar power systems, rechargeable batteries, wind turbines, hybrid systems with renewable sources (e.g., solar–wind–thermal), electrical power grids, combined heat and power systems, power plants, and commercial and residential buildings. The first article presents a multi-period integrated planning and scheduling approach for developing energy systems. The approach is applicable to energy systems with multiple resources, locations, processing pathways, and planning periods in which infrastructure decisions can be conducted. The applicability and effectiveness of the approach is shown using a case study examining the long-term development of a multi-site energy-intensive hydrogen-based energy system in Texas. The second article presents an optimal energy management for a hybrid water pumping system driven by a PV generator and a wind turbine. The proposed management system serves to guarantee the pumping system autonomy in a rural region where there is no access to the electrical network. A maximum power point tracking (MPPT) controller based on the fuzzy Takagi–Sugeno model is proposed to maximize power transfer to the pump in the presence of wind speed and insolation changes. The third article introduces a multi-objective economic-emission-dispatch optimization problem, the solution of which addresses hydro–thermal–solar–wind power scheduling arrangement in integrated PV, nonconvex thermal, and wind units. A heuristic optimization solver, named moth flame optimization, is used to solve the optimization problem. The optimization solution reduces the electric power generation cost and emission significantly. The fourth article uses linear matrix inequality techniques to find the maximum allowable time-delay bound for a proportional-integral (PI) controller used in a renewable-energy-based automatic generation control system, and it then studies the application of the technique in a deregulated environment including energy storage devices. The impact of communication delays, caused by the loss of synchronism between solar–wind and thermal unit, is evaluated by accounting for nonlinearities such as generation rate constraints, boiler dynamics, and governor with the dead band. The fifth article presents a single-objective economic replica of the short-term hydro-thermal scheduling problem in integrated power systems involving renewable solar and wind units. Solutions of the optimization problem obtained using an oppositional grasshopper optimization algorithm and several other algorithms including teaching learning-based optimization are compared. The sixth article studies the minimization of conflicting objectives such as fuel cost, load demand, and transmission losses subject to the constraints of renewable energy sources. A new optimization technique, namely a quasi-oppositional based whale optimization algorithm, is proposed and compared with several recently developed metaheuristic optimization techniques. The seventh article presents a new hybrid meta-heuristic algorithm entitled cross entropy-cuckoo search algorithm. The effectiveness of the proposed hybrid algorithm is demonstrated in solving the optimal power flow problem, considering renewable energy sources and controllable loads for different stochastic scenarios in a benchmark system to minimize the total operation cost. The eighth article introduces a hybridized metaheuristic optimization technique that is based on utilizing the exploration and stochastic property of elite opposition-based learning and chaotic maps. The hybrid algorithm is tested on various benchmark functions and compared with several other algorithms. The ninth article is on computationally efficient integrated design and predictive control of flexible energy systems. Specifically, the work studies a multi-fidelity black-box Bayesian optimization approach for integrated design and control of constrained nonlinear systems in the presence of uncertainties. The approach is applied to design a solar-powered building heating/cooling system (with battery and grid support) under uncertain weather and demand conditions with hourly variation over a year-long planning horizon. The 10th article presents an experimental investigation of the Harris Hawk optimization-based MPPT algorithm for a PV system under partial shading conditions. Simulation and experimental results showing the performance of the algorithm are presented. The 11th article proposes a hybrid method for parameter estimation in solar PV modules. Experimental results from different PV modules shows the performance of the estimation approach. The method can help solar PV module designers and simulators improve PV module performance. The 12th article proposes a hybrid technique by combining an evolutionary optimization technique, namely the modified invasive weed optimization with the conventional perturb and observe algorithm to enhance the search performance for the MPPT output of a PV system. The efficacy of the MPPT algorithm is demonstrated on a standalone PV system experimentally using a real-time microcontroller hardware setup. The 13th article presents a study of data-driven value-iteration control with application to wind turbine pitch control. The proposed method can handle uncertainties and does not require an accurate model. Simulation results show the effectiveness of the control method are presented. The 14th article presents a study of the use of economic nonlinear model predictive control (MPC) for wind turbine control. Wind turbine fatigue is accounted for in the cost function of the MPC. To handle the non-standard and discontinuous cost function, MPC is implemented sequentially. The performance of the MPC is compared with that of a conventional MPC. The 15th article deals with optimal decentralized control of a wind turbine coupled with a diesel generator system. The decentralized control system consists of two single-input single-output PI controllers. Simulation results are presented to compare the performance of the proposed controller with those of benchmark decentralized linear-quadratic Gaussian integral controllers of orders 4 and 11. The 16th article presents a study on MPC of a dual-fuel internal combustion engine (ICE) integrated with a waste heat recovery (WHR) system to support electric power demands in buildings. Control-oriented models of the WHR system and a turbocharged dual-fuel diesel-natural gas ICE are presented. This article reports that MPC minimizes the fuel consumption of the ICE and ensures the satisfaction of exhaust emission constraints. The 17th article presents an optimal design and scheduling study focused on the generation of renewable hydrogen and ammonia for combined heat and power systems in remote locations. A combined optimal design and scheduling model is used to minimize 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. The 18th article is on the optimal operation of a grid-connected battery energy storage system over its lifetime. Specifically, it focuses on Li-ion batteries and uses an empirical model to describe the battery degradation. The model includes an equivalent circuit for the battery and a simplified model for the power converter. To model the energy price variations, a linear stochastic model that includes the effect of the time-of-the-day is used. The problem of maximizing the revenues obtained over the system lifetime is formulated as a stochastic optimal control problem with a long, operation-dependent time horizon. Numerical simulation results show the effects of the energy loss parameters, degradation parameters, and price dynamics on the optimal policy. The 19th article deals with optimal hybrid control of interarea oscillation in power system using energy storage systems. It presents a rechargeable battery model that describes the battery energy storage on the d-q axis. The battery model is augmented into a two-area four-machine power system. It designs and applies an optimum hybrid controller based on linear quadratic regulator techniques to damp generators' frequency deviations. Simulation results show that the interarea oscillations are damped without losing the voltage stability of the system. The 20th article presents an application of MPC to achieve energy-efficient cabin climate control of electric vehicles (EVs). Specifically, a linear-time-varying MPC approach is presented for energy-efficient cabin climate control of EVs. The article reports that the MPC approach outperforms a rule-based controller in terms of response time and energy consumption. The 21st article focuses on nonlinear multi-objective and dynamic real-time predictive optimization for optimal operation of baseload power plants under variable renewable energy. Specifically, it investigates a multi-objective and dynamic real-time optimization framework to address the cycling of large-scale power plants under renewable penetration. The framework is applied to a baseload coal-fired power plant with post combustion CO2 capture. The 22nd article proposes the use of a new integral critic learning method for optimal tracking control of boiler-turbine systems. Specifically, a self-learning state tracking controller with a cost function is developed. A policy iteration-based integral reinforcement learning method is introduced to obtain the optimal control law. The simulation results show that the controller can provide satisfactory performance and can outperform MPC. The 23rd article is on PI control of a steam turbine in a natural gas combined cycle power plant. The controller is tuned using several optimization algorithms, including particle swarm optimization, artificial bee colony, genetic algorithm, gray wolf optimization, equilibrium optimization, atom search optimization, coronavirus herd immunity optimization, and adaptive neuro-fuzzy inference. Results show that a PI controller tuned with adaptive neuro-fuzzy inference outperforms PI controllers tuned with other methods. The 24th article deals with the building-to-grid optimal control of an integrated micro-concentrated solar power (Micro-CSP) and building heating, ventillation and air conditioning system for optimal demand response services. Specifically, the potential of the Micro-CSP to provide ancillary power to grids is investigated. It also demonstrates how the Micro-CSP can help buildings deal with constraints related to load peak shaving and ramp-rate reduction. The 25th article proposes a neuro-adaptive-optimal control scheme, based on simultaneous online system identification and control, to replace power system stabilizers in the renewable-energy-penetrated power systems. This work shows that the proposed control scheme provides improved oscillation-damping performance over a wide range of operating conditions and disturbances, in comparison with several well-established methods. The 26th article proposes a control method that uses a fractional-active-disturbance-rejection-controller to achieve load frequency control and automatic voltage regulation in a hybrid power system. The control method has better performance in terms of system stability, computational efficiency, and accuracy, compared to several other control methods. The 27th article centers on load balancing and optimal reactive power management using the butterfly optimization algorithm (BOA). A method that lacks the drawbacks of BOA and generates a better trade-off between exploration and exploitation abilities is proposed. Results indicate that the proposed method is capable of solving real world optimization problems. The 28th article offers a modified differential evolution algorithm that can solve the reactive power management problem. The application of the proposed algorithm to different standard test bus systems with varying active and reactive loading shows significant reduction in transmission losses. The 29th article applies a machine learning based generalized neural network estimator and a Takagi-Sugeno fuzzy control system to improve the performance of a dynamic voltage restorer. Results show that the proposed method outperforms several classical techniques, in terms of convergence speed with fewer tuning parameters, training time, and risks of local entrapment. The 30th article examines different metaheuristic algorithms for optimizing the gains of dynamic voltage restorer PI controllers. It reports that the controller tuning parameter values calculated by an ant-lion optimization technique provide better performance than those calculated by whale optimization, gray wolf optimization, and particle swarm optimization. The 31st article presents a study on the use of a chaos-assisted sine-cosine algorithm for optimally scheduling integrated hydro-thermal-wind-solar systems. It compares the effectiveness and robustness of the algorithm and several exiting methods by applying them to an integrated power plant consisting of eleven (two wind, two solar, four hydro, and three thermal) power generating units. [Correction added on 15 February 2023, after first online publication: this paragraph has been added in this version.] We would like to thank all the authors for their contributions to this special issue and Wiley for supporting this issue.