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The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network. • Holistic energy hub engages in upstream electricity and heat markets, optimizing profit. • Empowered energy hub actively influences markets with optimized strategies. • Robust optimization ensures stable operations in uncertain renewable sources. • Advanced load management optimizes profiles, aligning consumption with pricing. • Particle swarm optimization algorithm enhances search for optimal solutions. [ABSTRACT FROM AUTHOR]

The maturation of distributed energy resources (DER) has prompted the exploration of their deployment in commercial building applications due to their potential to supply energy at lower costs and emissions rates compared to centralized generation. While several software tools exist for evaluating the techno-economic potential of integrated renewable energy and combined heat and power (CHP) systems for distributed generation applications, many suffer from poor accuracy in capturing off-design (part load and changes in ambient air temperature and pressure) performance characteristics of microturbines, combustion turbines, or internal combustion engines. Thus, this paper presents a methodology for integrating these off-design characteristics in the mixed-integer linear program within REopt, a hybrid DER screening tool. The economic impact of the CHP off-design performance is observed through several application studies of various hybrid system configurations in different climates. Each study indicates how CHP off-design performance influences optimal sizing and dispatch decisions and therefore overall system economic value. We observe through case studies that modeling without the off-design effects, depending on the CHP prime mover and site, can result in Net Present Value predictions of hybrid systems that can be overoptimistic in frequently hot climates (up to 52 %), too conservative in frequently cold climates (up to 11 %), or unaffected (＋/−1 %) in temperate climates. Cases also highlight several advantages of hybrid systems relative to non-hybrid systems such as total economic value and the systems' ability to mitigate potentially negative consequences attributed to off-design performance. • A methodology for off-design performance as a function of load and ambient conditions is presented. • Scenarios where different application climates affect optimal sizing and dispatch are provided. • Not including off-design CHP modeling can result in overly optimistic or conservative economics. • Higher economic values for hybrid systems compared to either renewable energy or CHP-only systems. [ABSTRACT FROM AUTHOR]

As a large number of renewable energy resources are connected to power systems, the operation, planning, and optimization of power systems have been becoming more and more complex. Power flow calculation, unit commitment, economic dispatch, energy pricing, and power system planning are essentially computation problems. A lot of computing resources are required for these problems, which are non-trivial, especially for large-scale power systems with the high penetration of renewable energy. Traditionally, the calculation and optimization of power systems are completed by classical computers based on the classical computing theory and the von Neumann architecture. However, with Moore's law getting closer and closer to the limit, the importance of quantum computing has become increasingly prominent. Quantum computing has been applied to some fields to a certain extent, yet the applications of quantum computing in power systems are rare. As the power industry is the foundation of the national economy, introducing quantum computing into the power system has far-reaching and crucial significance, such as improving the penetration of renewable energy, enhancing the computing efficiency, and helping in achieving the goal of net zero and climate neutrality by 2050. This paper first introduces the core concepts, essential ideas and theories of quantum computing, and then reviews the existing literature on the applications of quantum computing in power systems, and puts forward our critical thinking about the applications of quantum computing in power systems. In brief, this paper is dedicated to a tutorial on quantum computing targeting power system professionals and a review of its applications in power systems. The main contributions of this paper are: (1) introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor; (2) based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams; (3) elaborate and derive some critical quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems; (4) critically summarize and comment on the existing literature on the applications of quantum computing in power systems; (5) the future applications and challenges of quantum computing in power systems are prospected and remarked. • Introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor. • Based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams. • Elaborate and derive some important quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems. • Critically summarize and comment on the existing literature on the applications of quantum computing in power systems. • The future applications and challenges of quantum computing in power systems are prospected and remarked. [ABSTRACT FROM AUTHOR]

• Electric vehicles charging and refueling with various renewable-based designs. • Globally applicable multi-design framework identifies the most sustainable design. • Uses a hybrid multicriteria decision method with a microgrid optimization tool. • Shows real-time case study with comparison of seven 100% renewable-based designs. • Optimal design avoids 2,455 tons of yearly carbon emissions compared with the grid. The rapid development of green charging and refueling stations is vital for the widespread adoption of fuel cell electric vehicles and battery electric vehicles. This paper investigates the challenge of selecting the most optimal autonomous station design among various 100 % renewable-based options, considering techno-economic and socio-political factors. A globally applicable systematic framework is presented, encompassing 14 performance criteria, a microgrid optimization tool, and a hybrid multicriteria decision method. Focusing on the pressing needs of Karachi, Pakistan, the study showcases the application of the framework to evaluate 7 renewable-based autonomous designs. Among these, the photovoltaic and biogas generator-powered station emerges as the most optimal option for Karachi, with a levelized cost of energy (LCOE) of 0.31 $/kWh, levelized cost of hydrogen (LCOH) of 4.281 $/kg and payback period of 8.3 years. The optimal design avoids 2,455 tonnes of carbon dioxide (CO 2)/year compared to the grid and 5,642 tonnes of CO 2 /year compared to diesel generators. When compared with the grid extension, the breakeven distance is found to be 4.6 km. Sensitivity analysis shows a 3.6 % decrease in LCOE and a 13.7 % decrease in LCOH with a 20 % increase in charging and refueling load, highlighting scalability benefits. This study contributes to various united nations' sustainability goals (7, 8, 9, 11, 12, 13), striving to provide a clear pathway for selecting optimal station designs. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

Due to the increase in electrical and cooling demands, the use of renewable resources for supplying these demands are subjected to more attention in recent years. This is while in the hot months of the year, due to the weather conditions, the need for electricity and cooling is increased. In this paper, a hybrid solar/wind system is proposed to satisfy the electrical and cooling demands, and hot water consuming of a building. Cooling demand is covered by combining absorption and compression chillers; so that, the primary energy of the compression chiller is supplied by a wind turbine and photovoltaic (PV) panels and the required thermal energy of the absorption chiller is supplied through evacuated tube collectors (ETCs). Considering the space limitation for installing solar systems, the main issue of this paper is focused on allocating the best space share for each of the PV panels and ETCs (in a case study building, 100 m2 of area is considered as a free space for solar systems). Three cases are assumed for the share of installation space as: 75% of area for ETC and 25% for PV (case 1), half share for both (case 2), 25% of area for ETC and 75% for PV (case 3). All of the defined cases are investigated by developing a comprehensive computational code based on energy, exergy, and economic analyses. The results show that although the first case with energy and exergy efficiencies of 50.60% and 18.5% has the best performance, the third case, with the highest net present value (NPV) has a payback period of nearly 7 years, is the eco-feasible case. Finally, by applying 7 different weights for the defined objective functions (energy efficiency, exergy efficiency, and NPV), a multi-objective optimization is applied to select the best system operation depending on the design requirements. Considering equal weight coefficients, the optimization results reports the case 3 as the optimum configuration. • A multi-generation system for providing electricity, cooling and hot water consuming is proposed. • The wind turbine, ETC and PV collectors and absorption and compression chillers are used. • Three defined cases are assumed for the share of collector installation space. • Energy and exergy efficiencies for first case are obtained 50.60% and 18.5%, respectively. • TOPSIS multi-objective optimization method is applied for selecting the best case. [ABSTRACT FROM AUTHOR]

Due to the stochastic characteristics of wind, keeping high-quality power into the grid-dependent hybrid wind energy systems is important for effective energy utilization, especially when there are non-linear loads involved. In this paper, a dual layer random features kernel mean p-power (MRFKMP) adaptive control for the grid interfaced converter of a DFIG-PV based hybrid energy system is implemented for the purpose of maintaining power balances and quality power at both the source and grid ends while operating at unity power factor. The introduced control extracts the weight of stator and load current components with finer convergence and lower computational complexity, excluding complex reference transformations. The dual layer adaptive control improves grid side converter functionality by improving power quality, interruption of loads, taking into account reactive power and harmonic remediation under steady-state and dynamic scenarios. In the secondary step, to achieve optimal DFIG-PV control response, the stability of the DC-link, which is the main coupling element for power transfer between DFIG, solar PV, and battery energy storage (BES), is reinforced by tuning the DC link voltage controller with the white shark optimization (WSO) algorithm. Under the worst-case scenario of large step wind change, the primacy of the envisaged WSO algorithm for tuning DC-link PI controller has been evidenced. It is discovered that WSO tuning positively affects the settling time and oscillatory response of the DC-link voltage not just at the outset, but also throughout the subjugated dynamic wind speed change. Power flow to the grid is stable due to optimized DC-link voltage performance. WSO parameter tuning improved efficiency is validated using MATLAB simulation results and compared to GA and PSO techniques in this study. [ABSTRACT FROM AUTHOR]

This paper presents a grid-connected hybrid renewable energy integrated with a reverse osmosis desalination plant to provide fresh water for a residential community. The hybrid energy system comprises a photovoltaic module and wind turbine as the main source of energy, and battery storage systems or hydrogen storage systems are used as an energy storage system, while a diesel generator is used as a backup energy source. A new multi-objective hybrid Particle Swarm Optimization – Grey Wolf Optimizer (PSO–GWO) optimization method is used to obtain the optimal size of the different system components to minimize both the total cost of fresh water production and, at the same time, CO 2 emissions, for a period of 20 years. Moreover, a comparison is done between the PSO–GWO optimization method and the use of PSO alone and the use of GWO alone. The solar radiation, temperature and wind speed of the residential community are measured using the weather station system. A comparison between different hybrid system configurations using three different optimization methods is presented. The complete model for energy management strategies and the optimization models for this study are programmed using MATLAB software. The results show that the proposed PSO–GWO hybrid performs better at determining the optimization parameters than either of the same optimization methods used in isolation. The optimization results indicate that a battery storage system is more economical than a hydrogen storage system. Further reduction in cost can be achieved by incorporating a diesel generator into the hybrid system. Finally, sensitivity analyses are performed to show how varying certain parameters affects total investment cost. Such analyses have shown that the variation in annual solar irradiance has a greater impact on the total investment cost than wind speed variation. [ABSTRACT FROM AUTHOR]

Energy storage (ES) is an important device to ensure operation stability and efficiency of a renewable energy based distributed energy generation (DEG) system. As such, many researchers have modelled the operation (scheduling) of energy storage in a DEG system, where it is mostly portrayed to operate on a daily cycle. In this paper, an analysis of the operation mode of energy storage is presented. Two modes of operation are defined, daily mode (DM) (ES operates in daily cycles) and weekly mode (WM) (ES operates in weekly cycles) which are modelled accordingly. This paper then attempts to analyse and compare between both modes in term of operation and cost. The analysis is performed through a mixed integer linear programming (MILP) model programmed via the General Algebraic Modelling System (GAMS). In general, with high capital cost of ES, it is advisable to program the ES to operate in a DM. The reason to this conclusion is mainly due to three factors, intermittency of renewable resources, varying weather conditions, and discharge rate of ES(s). [ABSTRACT FROM AUTHOR]

Energy crisis and environmental issues have encouraged the adoption of electric vehicle as an alternative transportation option to the conventional internal combustion engine vehicle. Recently, the development of smart grid concept in power grid has advanced the role of electric vehicles in the form of vehicle to grid technology. Vehicle to grid technology allows bidirectional energy exchange between electric vehicles and the power grid, which offers numerous services to the power grid, such as power grid regulation, spinning reserve, peak load shaving, load leveling and reactive power compensation. As the implementation of vehicle to grid technology is a complicated unit commitment problem with different conflicting objectives and constraints, optimization techniques are usually utilized. This paper reviews the framework, benefits and challenges of vehicle to grid technology. This paper also summarizes the main optimization techniques to achieve different vehicle to grid objectives while satisfying multiple constraints. [ABSTRACT FROM AUTHOR]

In recent years, sustainability receives greater attention due to increasing demands and limited resources worldwide. The Indian state of Uttarakhand is rich in the availability of renewable energy resources such as solar, micro hydro, biomass, wind etc. Optimum utilization of these resources in stand-alone mode has been recognized as an economical and efficient option compared to grid extension for electrification of remotely located rural households. This paper presents a combined techno-economic optimization and energy management of a stand-alone solar-micro hydro-biomass-wind based IRES (Integrated Renewable Energy System) to meet the electrical energy demand of cluster of village hamlets of Uttarakhand state. A load shifting based DSM (demand side management) strategy has been suggested in the paper for energy management of the considered IRES. Further, optimization results without and with DSM strategy have been presented and compared. Finally, a sensitivity analysis has also been performed to evaluate the impact of different parameters on the considered system. [ABSTRACT FROM AUTHOR]

This paper presents a review of the optimization techniques and strategies applied to wind turbine performance optimization. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the published literature. The current energy demand combined with depletion of fossil-fuel reserves and stricter environmental regulations have led to the development of alternative renewable energy solutions like wind energy. The current 2030 United States target is to have at least 20% of the US energy supply by onshore and offshore wind farms. To meet these demands, wind energy costs have to be able to compete with traditional fossil fuel sources. Hence, it is essential and vital that wind turbine designers and manufactures search the optimal solution that fits the objectives under a set of design constraints. Throughout the last 30 years, the objective function has evolved from the earlier maximized metric of the power coefficient to the maximization of the annual energy production. Common alternatives such as blade mass minimization and maximization of the rotor thrust and torque have been examined. However, the main objective has been focused on the minimization of the cost of energy in order for wind energy to become more competitive and economically attractive. The purpose of this paper is to review previous work that undertakes the performance optimization of horizontal wind turbines by highlighting the main aspects when tackling the wind turbine optimization problem such as: objective functions, design constraints, tools and models and optimization algorithms. In addition, in a conclusion of the review, a discussion and argument about the challenges, issues and future developments are identified. [ABSTRACT FROM AUTHOR]

Microgrid systems, such as solar photovoltaic (PV) power and wind energy, integrated with diesel generators are promising energy supplies and are economically feasible for current and future use in relation to increased demands for energy and depletion of conventional sources. It is thus important to optimize the size of hybrid microgrid system (HMS) components, including storage, to determine system cost and reliability. In this paper, optimal sizing of a PV/wind/diesel HMS with battery storage is conducted using the Multi-Objective Self-Adaptive Differential Evolution (MOSaDE) algorithm for the city of Yanbu, Saudi Arabia. Using the multi-objective optimization approach, the objectives are treated simultaneously and independently, thereby leading to a reduction in computational time. One of the main criteria to consider when designing and optimizing the HMS is the energy management strategy, which is required to coordinate the different units comprising the HMS. The multi-objective optimization approach is then used to analyze the Loss of Power Supply Probability (LPSP), the Cost of Electricity (COE), and the Renewable Factor (RF) in relation to HMS cost and reliability and is tested using three case studies involving differing house numbers. Results verify its application in optimizing the HMS and in its practical implementation. In addition, optimization results using the proposed approach provided a set of design solutions for the HMS, which will assist researchers and practitioners in selecting the optimal HMS configuration. Moreover, it is important to select optimally sized HMS components to ensure that all load demands are met at the minimum energy cost and high reliability. [ABSTRACT FROM AUTHOR]

This paper presents results on simulation, optimization and control of hybrid solar based energy system to power a desalination plant. The principal objective is to design a clean energy system to meet the desired electric load of the desalination plant with high renewable fraction, low cost of energy, and low carbon dioxide gas emissions. Hourly simulations and optimization were performed to determine the performance and life cycle cost of the different hybrid power configurations. The results of the baseline or the actual power system from the grid are compared with two new renewable power systems: (1) grid tied solar system: solar PV/grid/inverter power system, and (2) Off grid solar power system: PV/diesel generator/battery/inverter power system. The results show that the solar PV/grid/inverter power system offers the best performance compared to PV/diesel generator/battery/inverter. The total energy from the hybrid grid tied solar system is used to meet the AC load of the desalination plant with almost no excess electricity and power shortage. The proposed hybrid power system for the desalination plant is sustainable, economically viable and environmentally friendly: high renewable fraction (47.3%), low excess power (0.15%), low levelized cost of energy (90 $/MWh), and low CO 2 gas emissions (264.25 kg CO 2 /MWh). [ABSTRACT FROM AUTHOR]

Ground source heat pump systems that are installed in areas with heating or cooling dominant seasons, or in buildings with utilization characteristics that lead to a disparity in demand, often encounter challenges related to ground thermal imbalance. This imbalance can lead to long-term ground temperature changes and may cause premature system failure. This paper focuses on combining a ground source heat pump system with a solar thermal array, with the goal of eliminating the effect of ground thermal imbalance, and minimizing system lifetime cost. A thermal mass ground heat transfer model is combined with a time-stepping model to analyze the system for a variety of solar array sizes. The details associated with this modelling technique are presented, and case studies are provided to illustrate the results of the calculations for three different buildings. It is shown that increasing the solar array size can offset ground thermal imbalances, but increasing the array size also results in a larger initial system cost. An economic analysis is then carried out to determine the system lifetime cost as a function of this solar array size, and an optimal array size from an economic perspective was found. The result of the study shows that hybridizing a ground source heat pump system with a solar array produces a viable system from a technical and economic standpoint, can be used to avoid premature system failure, and can reduce system lifetime cost. [ABSTRACT FROM AUTHOR]

Ammonia is a critical chemical to fertilize plants and feed the world. Recently, a first of its kind renewable ammonia plant has been built in Morris, MN, powered by wind energy. A broader deployment of such renewable plants will require careful selection of location and unit sizing to keep costs low and optimize the entire ammonia supply chain. In this paper, a 48-h receding horizon optimization problem is developed to optimally schedule unit set points for the system in order to minimize annual operating costs. This optimization formulation is then used to examine how the optimal operating cost depends on the key design parameters of location and unit size. Using the results obtained, simple correlations are developed which capture the dependence of operating costs on ratios of unit capacities, properly scaled to remove the dependence on location. [ABSTRACT FROM AUTHOR]

This paper seeks to quantify the benefits of a flexible energy system in the context of enabling higher levels of variable renewable energy on the grid. We explore a nuclear hybrid energy system (NHES) consisting of a 300 MW small modular reactor, wind generation, battery storage, and a reverse osmosis desalination plant. A dispatch rule is constructed within the Risk Analysis Virtual Environment (RAVEN) to model the system. Stochastic optimization and parametric analysis are utilized to explore how increased volatility in the net demand resulting from higher levels of wind penetration affect the optimal solution, and the stability of the system’s levelized cost of electricity (LCOE). In this study, net demand is the demand minus wind generation. This work contributes multi-objective analysis implemented through a supply-demand mismatch penalty to illustrate the financial stability and operational reliability benefits of the flexible energy system. In this context, we find that the additional up front cost of flexible loads and energy storage result in greater stability in LCOE as volatility in the demand increases. Additionally, the flexibility results in increased reliability in terms of meeting the demand. Although the analysis is conducted on a NHES, we emphasize the flexibility of the method applied here, in that the RAVEN platform and the multi-objective strategy are widely applicable to the analysis of energy systems faced with uncertainties in supply and demand. [ABSTRACT FROM AUTHOR]

This paper presents planning and optimization of standalone DC microgrids for rural and urban applications in India. Load profiles in the form of a rural village, an urban residential building and a business organisation are considered for the study. Using these load profiles, different microgrid configurations with generation from PV, wind turbine and biodiesel generators are constructed. Optimization studies are then carried out using Homer Energy software to determine the optimal system configuration which has the lowest cost of energy. Historical data of hourly solar irradiation and wind speed were also used in the study to obtain a realistic result. A special case study for the rural scenario with a partial connectivity to the grid is also conducted which is reminiscent to the poor reliability of supply in India. The study also gives a detail comparison between the various cost components, electrical productions, energy management and emissions for each of these systems. The results of optimization show reasonable cost of electricity for reliable supply of power for the various load configurations as compared to the actual grid prices. [ABSTRACT FROM AUTHOR]

Off-grid power systems represent one of the key solutions for rural electrification. Most of the analyses or tools already present in literature do not consider the final users' energy needs as the starting point of the process nor they consider the inherent uncertainties about loads and resources. Comprehensive stochastic procedures that look at the atypical features of rural contexts and include estimation errors into the design phase are strongly required. In this paper, we present Poli. NRG (POLItecnico di Milano - Network Robust desiGn): a novel software package for the robust design of off-grid electric power systems. Poli. NRG is composed of four blocks which separately face the different design phases: (i) the data inputs gathering block provides a methodology to collect field data as regards weather condition and load demand; (ii) the inputs processing block elaborates the inputs to obtain load and sources profiles over the entire lifetime of the plant; (iii) the system modelling and simulation block simulates different off-grid system configurations and evaluates the related techno-economic performances; (iv) the output formulation block finds the most robust design for the targeted context through specific optimization methods. After a comprehensive description of the software, we have applied it to size a PV + BESS microgrid system to supply power to a peri-urban area of Uganda. The results confirm that parameters' uncertainties deeply affect the design of the system and motivate the robust design approach proposed. Poli. NRG is devoted to map those uncertainties and provide information for decision makers. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of alternative, long-term energy supply strategies for the Ethiopian power sector, during 2015–2045, using the MARKAL energy system model. This study also identifies alternative, sustainable energy supply options to meet Ethiopia's rising demand for energy, while also achieving the policy goals of universal electrification, zero greenhouse gas emissions, increased electricity exports, and improved energy security. In all scenarios, the results show a large potential for renewable energy technologies, such as hydropower, solar PV and wind. These technologies have implications for neighboring countries in the region and will also affect the agriculture sector in Ethiopia. Hydropower, for example, is a renewable energy source that can contribute to rural electrification, while also providing water to support irrigation expansion. An alternative policy scenario prioritizing renewable energy technologies reduces dependence on fossil fuel completely at minimal cost, while providing long-term environmental benefits. Expansion of electricity access to the entire population entails large investments in power generation capacity as well as substantial increases in the total system cost of energy production. Such a scenario would also increase the country's reliance on fossil fuels and geothermal energy sources. Most alternative policy options show higher investment costs will be required to achieve policy goals in the near term (with the exception of the export scenario). However, the analysis shows long-term benefits from investing in energy supply including sustainable energy system development, expansion of access to modern sources of energy, and the development of a low carbon society. [ABSTRACT FROM AUTHOR]

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15 % , 3.29 % , 3.62 % , 9.2 % , 5.74 % , and 2.43 % for the Perth, Adelaide, Sydney, Tasmania, Brisbane, and Darwin wave sites, respectively. Our investigations reveal that the best-found arrangement at the Tasmania wave site was able to absorb the highest level of wave power relative to the other locations. • A new Multi-swarm Cooperative optimisation framework is proposed to optimise wave farms performance. • A new fast surrogate model is developed to speed up expensive optimisation process of wave farm. • To improve the placement of converters, a symmetrical backtracking search algorithm is proposed. • To handle the infeasible wave converters layout during the optimisation, a new and effective repair function is developed. • The proposed optimiser enhances significantly the total power output at six wave farms compared with previous methods. [ABSTRACT FROM AUTHOR]

Abstract: Photovoltaic (PV) generation of electricity is an important renewable energy source. One way to increase the value of small-scale renewable generators is to aggregate and control their output so they appear to the wider grid as one larger generator. This paper introduces our Virtual Power Station system which aggregates multiple geographically dispersed small-scale photovoltaic generators together to present to the wider electricity system as a single dispatchable quantity in an energy market. As such a quantity has greater benefit to the wider system than the individual responses of many uncoordinated energy sites, the Virtual Power Station can improve the payback period for renewable energy systems. This paper describes a Virtual Power Station deployment with a diverse range of sites, and PV output, which are then coordinated together with an intelligent battery controller. We also introduce an optimal control strategy that manages the energy storage to allow a firm commitment of output energy from the Virtual Power Station. [Copyright &y& Elsevier]

District heating and cooling (DHC) systems are attracting increased interest for their low carbon potential. However, most DHC systems are not operating at the expected performance level. Optimization and Enhancement of DHC networks to reduce (a) fossil fuel consumption, CO2 emission, and heat losses across the network, while (b) increasing return on investment, form key challenges faced by decision makers in the fast developing energy landscape. While the academic literature is abundant of research based on field experiments, simulations, optimization strategies and algorithms etc., there is a lack of a comprehensive review that addresses the multi-faceted dimensions of the optimization and enhancement of DHC systems with a view to promote integration of smart grids, energy storage and increased share of renewable energy. The paper focuses on four areas: energy generation, energy distribution, heat substations, and terminal users, identifying state-of-the-art methods and solutions, while paving the way for future research. [ABSTRACT FROM AUTHOR]

Many cities around the world have reached a critical situation when it comes to energy and water supply, threatening the urban sustainable development. From an engineering and architecture perspective it is mandatory to design cities taking into account energy and water issues to achieve high living and sustainability standards. The aim of this paper is to develop an optimization model for the planning of residential urban districts with special consideration of renewables and water harvesting integration. The optimization model is multi-objective which uses a genetic algorithm to minimize the system life cycle costs, and maximize renewables and water harvesting reliability through dynamic simulations. The developed model can be used for spatial optimization design of new urban districts. It can also be employed for analyzing the performances of existing urban districts under an energy-water-economic viewpoint. The optimization results show that the reliability of the hybrid renewables based power system can vary between 40 and 95% depending on the scenarios considered regarding the built environment area and on the cases concerning the overall electric load. The levelized cost of electricity vary between 0.096 and 0.212 $/kW h. The maximum water harvesting system reliability vary between 30% and 100% depending on the built environment area distribution. For reliabilities below 20% the levelized cost of water is kept below 1 $/m 3 making competitive with the network water tariff. [ABSTRACT FROM AUTHOR]

The concepts of the biorefinery and microgrid have emerged as ways to increase the sustainability of the energy infrastructure. Although typically considered as separate systems, synergies exist between the biorefinery and the microgrid, suggesting that a combined system could be more efficient than the individual systems. This paper explores this hypothesis by comparing the optimal designs and costs of the individual, and the combined biorefinery and microgrid systems. A novel design optimization problem considering synergistic operation of the biorefinery and microgrid is presented. A solution method to this problem is developed that exploits the separable nature of the optimization of such a ”system of systems.” Base case results show that the combined system costs are higher than those of the individual systems. However, by implementing a hydrogen recycle, significant savings are seen in the combined system, highlighting a direct advantage of system synergy. The effects of energy autonomy on the system are also analyzed and discussed. The overall analysis shows that the synergies between the biorefinery and microgrid can be exploited to create an energy system that is less costly and more efficient than the sum of its constituent parts. [ABSTRACT FROM AUTHOR]

High energy efficiency for low operating cost of desalination can be obtained from operational optimizations as well as component-wise innovations. This paper is concerned with an energy-efficient operation for reverse osmosis (RO) desalination process involving a joint control of multiple modules. Energy resource considered for desalination is of two types: intermittent solar energy and steady grid energy. With solar energy, the energy efficiency is measured by total permeate production obtained while the solar energy is available. With grid energy, the energy efficiency is measured by the power consumed to get unit permeate production rate. Achieving maximum energy efficiency is equivalent to solving constrained optimization. For the constrained optimization, obligatory constraints are given by the permissible ranges of trans-membrane pressure on RO membrane and total dissolved solids of permeate, and optional constraint is given by the permissible range of permeate production rate. Constraints as well as the objective function are modeled by the empirical second order equations in terms of control variables of multiple modules. Jointly optimal values of the control variables are found by the sequential quadratic programming. Experimental results obtained by the jointly optimal control demonstrate superior performances as compared to those acquired by marginally optimal control schemes. [ABSTRACT FROM AUTHOR]

This paper aims to address the issues related to renewable energy (RE) resources optimization in the areas where providing power from main grid is challenging. A region having ten different sub-regions has been considered for the optimization based on transportation algorithm. The partitions are made based on customer segmentation to ensure that various scattered demand for the RE is represented at the best location to enhance energy optimization by minimizing the energy loss during transmission. Three cases, i.e., when the demand and supply are equal, when there is more demand than supply, and when the supply is greater than demand are considered for the analysis. For the first case, the total energy requirement and the energy potential available from the regions is 1.26 GW h/year. For this scenario, most of the regions (seven regions) received the energy requirements from single RE source; three regions received the required energy from two different RE sources. The annual optimized total cost of energy supply for this scenario is determined to be $187,961.68. Results obtained from the transportation model have been validated based on other RE studies in the area. For the similar case study considered, it is noted that the minimized total cost obtained using transportation algorithm depicts an improvement in cost over integrated renewable energy system (IRES) models. The developed optimization model could be used as a decision making support tool to evaluate and select various alternative renewable energy resources and to determine the optimal locations for developing these resources. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the design and assessment of a renewable energy sources (RES)-based hydrogen supply system. We generate an integrated renewable hydrogen supply (IRHS) system that includes various types of RES and hydrogen technologies such as production, storage, and transportation. We then develop a multi-period deterministic optimization model for the IRHS system by using a mixed integer linear programming (MILP) technique. The proposed model optimizes the configuration and operational strategy of the IRHS system. The capability of the proposed model is demonstrated through its application to the design problem of Korea's future hydrogen economy. We perform an economic evaluation to identify the major parameters for the required cost of the energy system infrastructure. [ABSTRACT FROM AUTHOR]

Increasing the use of variable renewable electricity (VRE) such as solar and wind power will impose major challenges to existing energy systems. The increased variability needs to be considered, but also how to optimally operate the remaining power system. In this paper, we present a fast and easy-to-use optimization model to find cost-optimal ways to manage the energy system with large-scale VRE. This is done by minimizing the running costs and maximizing revenues of the energy production. The model accounts for the energy system dynamics. It can handle both electric and thermal loads at different scales (building, city) and allows incorporating different flexibility strategies. The model is applied to analyzing a very large wind power scheme for Helsinki city (Finland), coupled to the existing CHP-dominated district heating schemes through power-to-heat conversion for more optimal system integration leading to major CO 2 emission reductions. The model is well suited to studying integration of renewable energy, to design better policies for introducing VRE and to find solutions to increase energy system flexibility, among others. [ABSTRACT FROM AUTHOR]

It is imperative to explore the full carbon dioxide-saving potential for plug-in hybrid electric vehicles. This paper seeks to examine the role of renewable energy and powertrain optimization in minimizing daily carbon emissions of plug-in hybrid electric vehicles. A spectrum of influencing factors are investigated, including charging protocol, timing, on-road power management strategy, battery size, and carbon-emission intensity of the grid. A high-efficiency convex programming framework is harnessed to optimize a plug-in hybrid powertrain. Two originally important contributions evidently distinguish this work from existing efforts. First, diverse heuristic scenarios and concomitant weaknesses are elucidated, and the carbon reductions arising from renewable energy integration and the convex programming framework are quantified. The great importance of their synergy is accentuated, i.e., swiftly adapting charging/power management controls to wind intermittency. Second, battery health implication is explored for the optimal and heuristic scenarios, via a dynamic battery State-of-Health model. [ABSTRACT FROM AUTHOR]

Recently, energy management systems (EMSs) and storage equipment have become increasingly important for optimizing operating schedules to reduce operating costs. Although many previous studies aimed to optimize the operating schedule of energy systems, some considered only perfect predictions for demand and photovoltaic (PV) power generation. In practice, however, predicted values are often different from actual phenomena. Thus, a practical method that can specify how to control each component under the uncertainty is needed. In this paper, we proposed a new optimization strategy to handle the uncertainty and called it the two-time steps recalculation strategy (TtsR). TtsR proposes a framework for problem formulation and functions in combination with an optimization method. Epsilon differential evolution (εDE) was adopted to handle efficiency nonlinear and constraint conditions. TtsR was compared to the all-time steps recalculation strategy (AtsR), which is considered an ideal strategy because it can obtain accurate solutions. The results showed that TtsR required lower computational time than AtsR to obtain a quasi-optimal solution under conditions of unpredicted changes in the power generation and demand; meanwhile, TtsR was able to maintain computational accuracy. [ABSTRACT FROM AUTHOR]

Renewable energy has known spectacular development in the last decades as a solution to the climate change effect. Wind energy is one of the most popular technologies for power generation. However, the variable operation and extreme meteorological conditions challenge integrating this technology, especially in cold climate regions. These conditions favor the blade's vibration and icing, leading to a drop in efficiency, high stop time, and even failure. This paper presents a solution to optimize the wind turbine performance in harsh meteorological conditions and under undesired vibrations. The method uses the piezoelectric patches to control the blade's vibration and deice its surface. An equivalence model is presented for the vibration control mode to approximate the variable section blade with a planar beam. In addition, we present the analytical model for the vibration control and a complete optimization of the piezoelectric actuator/sensor pair position and controller parameters using the genetic algorithm. Finally, the numerical model of variable section blade with piezoelectric actuator and accreted ice is presented for the deicing mode. The results show the system's efficiency in eliminating the blade's vibration quickly and destroying the ice bond with the surface as the generated transversal stress exceeds the ice-aluminum adhesion force. • Piezoelectric materials can be used to control the wind turbine blade vibration and deice its surface. • Piezoelectric patches can be added to the surface of the beam to reduce rapidly its vibration response. • Genetic Algorithm helps find the best values for the piezoelectric patches position and the controller parameters. • Piezoelectric patches can generate ultrasound waves that propagate in the structure and de-bond the ice from its surface. • Piezoelectric patches generate high transversal XZ stresses capable of eliminating the ice at the interface. [ABSTRACT FROM AUTHOR]

Abstract: There is an increasing need for new and greater sources of energy for future global transportation applications. One recognized possibility for a renewable, clean source of transportation fuels is solar radiation collected and converted into useable forms of electrical and/or chemical (hydrogen) energy. This paper describes methods for utilizing and combining existing technologies into systems that optimize solar energy collection and conversion into useful transportation fuels. Photovoltaic (PV)-electrolysis (solar hydrogen) and PV-battery charging systems described in this paper overcome inefficiencies inherent in past concepts, where DC power from the PV system was first converted to AC current and then used to power electrical devices at the point of generation, or fed back to the grid to reduce electricity costs. These past, non-optimized concepts included efficiency losses in power conversion and unnecessary costs. These drawbacks can be avoided by capitalizing on the unique feature of solar photovoltaic devices that match their maximum power point to the operating point of an electrolyzer or a battery charger without intervening power transformers. This concept is illustrated for two systems designed, built, and tested by General Motors for fueling a fuel cell electric vehicle and charging an automotive propulsion battery. Based on this research, we propose a scenario in which individual home-owners, businesses, or sites at remote locations with no grid electricity, can capture solar energy, store it as hydrogen generated via water electrolysis, or as electrical energy used to charge storage batteries. Such a decentralized energy system provides a home refueling option for drivers who only travel limited distances each day. [Copyright &y& Elsevier]

This paper defines the diverging interests of multiple actors in typically achieving and advancing energy efficiency. Gaps in the literature include the short elaboration of energy efficiency factors and conditions and the lack of information about developed and developing countries and thorough consideration of their specific features, impeding the development and implementation of detailed energy efficiency management strategies. It is against this backdrop that this paper seeks to identify the essence of energy efficiency and developing specific recommendations for achieving the most beneficial effect and well-balanced energy efficiency on the global scale, within the achievements of international goals in sustainable development. The paper exposes the critical factors of energy efficiency, accurately models the best energy efficiency characteristics, and settles the best energy consumption structure. The methodological purpose is to find different energy efficiency scenarios and disentangle fundamental differences in energy efficiency in developed and developing countries. The paper indicates that the reasonable likelihood of noteworthy achievement results in energy efficiency with insignificant mid-term changes. We give recommendations about well-balanced energy efficiency on the global scale and global progress in sustainable development. • This paper disentangles key differences in energy efficiency in developed and developing countries. • We identify the essence of energy efficiency and develop recommendations for achieving balanced energy efficiency globally. • The paper models the best energy efficiency characteristics and settles the best energy consumption structure. • We give recommendations about well-balanced energy efficiency in sustainable development. • The study shows energy consumption optimization in terms of GDP structure, energy consumption, and sustainability. [ABSTRACT FROM AUTHOR]

In this paper, the economic and environmental assessment of a system composed by a Smart Polygeneration Microgrid (SPM) and a Sustainable Energy Building (SEB) is performed, with specific reference to a pilot facility installed at the Savona Campus and managed by the University of Genoa Power Systems Research Team. The analysis includes the evaluation of operational costs and CO 2 emissions, and is carried out in two steps: firstly, the quantification of the economic and environmental benefits provided by the SPM and the SEB on a yearly timescale is determined. Then, the same analysis has been done on a short-term horizon by the use of an optimization algorithm included in the SPM supervisory software. Such a model minimizes an objective function related to daily operational costs and includes the SPM electric and thermal power plants and loads, the distributed heating network, an active building (SEB) and the ICT system responsible for the operational management of the whole research infrastructures. The results of the analysis and the performed experimental research contribute to develop a set of Key Performances Indicators (KPIs) suitable for the smart microgrid concept deployment. [ABSTRACT FROM AUTHOR]

The finiteness of fossil fuels implies that future electric power systems may predominantly source energy from fuel-free renewable resources like wind and solar. Evidently, these power systems without fuel will be environmentally benign, sustainable, and subject to milder failure scenarios. Many of these advantages were projected decades ago with the definition of the soft energy path , which describes a future where all energy is provided by numerous small, simple, and diverse renewable sources. Here we provide a thorough investigation of power systems without any fuel-based generation from technical and economic standpoints. The paper is organized by timescale and covers issues like the irrelevance of unit commitment in networks without large, fuel-based generators, the dubiousness of nodal pricing without fuel costs, and the need for new system-level models and control methods for semiconductor-based energy-conversion interfaces. [ABSTRACT FROM AUTHOR]

Osmotic power generation is one of the cleaner and sustainable methods for energy generation. This paper presents a study on the optimal size and design of pressure retarded osmosis (PRO) power plant as a viable source of renewable energy in the selected site in Bahmanshir river of Iran. The optimal sizing and design problem is formulated as a Non-Linear Programming (NLP) optimization problem and solved using the standard optimization software titled generalized algebraic modeling system (GAMS). The objective function is minimizing the total cost while considering the technical and economic constraints. Optimization results show that the optimal capacity of the PRO osmotic power plant is 77.8 MW. Economic analysis is performed for the obtained results and it was observed that construction of this type of power plants will not be attractive for private sector investors with the market energy prices (i.e. 8 cents/kW h).The effect of the future development in power density of membrane and membrane unit price in economic feasibility of the osmotic power plant is studied. [ABSTRACT FROM AUTHOR]

The future energy system in community level should be more ‘smart’ to secure reliability, enhance market service, minimize environmental impact, reduce costs and improve the use of renewable energy source (RES). Therefore, this paper proposes an energy integration system – smart hybrid renewable energy for communities (SHREC). It considers both thermal (heating and cooling) and electricity market in a large community level and highlight the interactions between them through utilizing RES, combined heat and power (CHP) and energy storages. A planning model based on CHP modelling is developed for the SHREC system. A linear programming (LP) algorithm is developed to optimize the SHREC system in a weekly period and the results are compared with an existing energy optimization software. We also demonstrate the model in a sample SHREC system during three typical weeks with cold, warm and mid-season weather in the year 2011. The results indicate that the developed modelling and optimization method is more efficient and flexible for the smart hybrid renewable energy systems. [ABSTRACT FROM AUTHOR]

This paper examines the impact of location and load shape selection on microgrid optimal design. 96 unique combinations of location and load shape are considered to provide a broader scope than any previous work in microgrid design sensitivity analysis. In addition, the level of autonomy from the macrogrid is considered as a tunable parameter in the optimization. A generic system is considered consisting of photovoltaics, wind turbine, microturbines, electric and natural gas boilers, thermal storage, and a battery bank. The microgrid is grid-connected and designed to supply both heat and power. A mixed integer linear program is used to minimize the expected cost of energy supply over a 20 year horizon. Trends in the design results are discussed and important input parameters that depend on the location and load shape are identified. Finally, a procedure to quantify these trends and predict optimal design results in new locations is proposed. [ABSTRACT FROM AUTHOR]

The share of the renewable energy sources (RES) in the global electricity market is substantially increasing as a result of the commitment of many countries to increase the contribution of the RES to their energy mix. However, the integration of RES in the electricity grid increases the complexity of the grid management due to the variability and the intermittent nature of these energy sources. Energy storage solutions such as batteries offer either short-term storage that is not sufficient or longer period storage that is significantly expensive. This paper introduces an energy management approach which can be applied in the case of power and desalinated water generation. The approach is based on mathematical optimization model which accounts for random variations in demands and energy supply. The approach allows using desalination plants as a deferrable load to mitigate for the variability of the renewable energy supply and water and/or electricity demands. A mathematical linear programming model is developed to show the applicability of this idea and its effectiveness in reducing the impact of the uncertainty in the environment. The model is solved for the real world case of Saudi Arabia. The optimal solution accounts for random variations in the renewable energy supply and water and/or electricity demands while minimizing the total costs for generating water and power. [ABSTRACT FROM AUTHOR]

Renewable energy technologies׳ systems are major components of the strategy to reduce harmful emissions and deal with depleting energy resources. It is necessary to deploy renewable energy sources in the best possible way such that cost is minimized and generation is maximized. In this paper, we present a review of different optimization methods for deployment and operation of renewable energy sources based generating units. Unlike other existing reviews, we carry out a general review of this research area, without limiting ourselves to any particular issue or geographic location. We examine this area with respect to different types of renewable energy sources, different modes of operations, types of objective functions for optimization and different geographical areas from which research publication are emanating. We present a general resource allocation problem and specify different possibilities for input, output, objective function and constraints. In addition, we review different objectives used in defining the optimization problems. We also present different types of linear and non-linear optimization algorithms used in renewable energy sources. Finally, we review optimization techniques for applications with respect to different end users. [ABSTRACT FROM AUTHOR]

Wind energy has become the world's fastest growing energy source. Although wind farm layout is a well known problem, its solution used to be heuristic, mainly based on the designer experience. A key in search trend is to increase power production capacity over time. Furthermore the production of wind energy often involves uncertainties due to the stochastic nature of wind speeds. The addressed problem contains a novel aspect with respect of other wind turbine selection problems in the context of wind farm design. The problem requires selecting two different wind turbine models (from a list of 26 items available) to minimize the standard deviation of the energy produced throughout the day while maximizing the total energy produced by the wind farm. The novelty of this new approach is based on the fact that wind farms are usually built using a single model of wind turbine. This paper describes the usage of multi-objective evolutionary algorithms (MOEAs) in the context of power energy production, selecting a combination of two different models of wind turbine along with wind speeds distributed over different time spans of the day. Several MOEAs variants belonging to the most renowned and widely used algorithms such as SPEA2 NSGAII, PESA and msPEA have been investigated, tested and compared based on the data gathered from Cancun (Mexico) throughout the year of 2008. We have demonstrated the powerful of MOEAs applied to wind turbine selection problem (WTS) and estimate the mean power and the associated standard deviation considering the wind speed and the dynamics of the power curve of the turbines. Among them, the performance of PESA algorithm looks a little bit superior than the other three algorithms. In conclusion, the use of MOEAs is technically feasible and opens new perspectives for assisting utility companies in developing wind farms. [ABSTRACT FROM AUTHOR]

Renewable generating systems are alternative to produce electric energy in a clean manner. However, the high costs of the constituents limit their broad use. Thus, sizing is an important issue in the renewable generating systems design, in order to reach an efficient relationship between cost and benefit. Likewise, the random nature of the sources makes the sizing a complex task with regard to a conventional system. This paper is focused on calculate the optimal size of a wind-photovoltaic-fuel cell system to meet the power demand of an isolated residential load located in the south-east region of Mexico (Chetumal city 18°31′21.4″N 88°16′11.3″W), with a solar radiation range from 0 to 0.75 kW/m 2 and wind speed range from 5 to 7.8 m/s. Swarm intelligence techniques have been successfully applied in solving many combinatorial optimization problems in which the objective space possesses many local optimal solutions. This work employs the Particle Swarm Optimizer (PSO) algorithm to search the optimal sizing for the power plant minimizing the total costs of the system; as a metaheuristic procedure, the PSO was able to find the best configuration regardless the lack of a deep knowledge of the problem. Compared against the Differential Evolution (DE) technique, the PSO performance is faster and able to provide a configuration that saves around 10% of the total cost of the hybrid system. [ABSTRACT FROM AUTHOR]

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables. • Three-step multi-period pinch-based methodology for energy planning is presented. • The model incorporates an actual billing system. • Time-sliced based optimization model is developed in the proposed framework. • Optimal emission reduction ratio and storage size are identified as 0.71 and 236 kWh. • The total electricity bill can be further reduced by 35%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three water/power generation configurations were designed, modeled, and optimized. • Damage to human health and the ecosystem caused by diesel generator were estimated. • Division algorithm was more accurate and faster than the genetic algorithms. • Desalination with photovoltaic panel/wind turbine was environmentally efficient. • Desalination with the photovoltaic panels was economically the best configuration. Although freshwater is necessary for the well-being of humankind, increasing population growth and limited resources lead to a serious crisis to supply freshwater. Since the Earth is surrounded by seawater, desalination based on electrical power is introduced as a promising technology to provide freshwater. It is well documented that the connection of remote areas that usually do not have access to freshwater into the electricity grid is not affordable and eco-friendly. Hence, the efforts to design and construct high reliability, cost-effective, and eco-friendly stand-alone hybrid renewable energy system in remote areas. In line with this, this paper describes a novel energy management system for the optimized operation of a stand-alone hybrid energy system based on photovoltaic panels, wind turbines, batteries, and diesel generator. For this purpose, a multi-objective optimization problem is formulated by combining three objective functions, i.e., minimum the total life cycle cost as well as environmental impacts on human health and ecosystems and the maximum system reliability that can conflict with each. To solve the multi-objective optimization problem, a division algorithm is proposed that is more flexible and faster compared with conventional algorithms such as genetic algorithm. In order to show the proposed framework, a real case study in Larak Island, Iran, with appropriate solar and wind is considered. The effectiveness of the applied approach compared with optimization results of genetic algorithm and the artificial bee swarm optimization algorithm that was previously used successfully to solve optimization problems related to desalination integrated with the renewable energy system. The optimization is performed based on different diesel fuel price amounts (0.2, 0.5, and 1 $/liter). It is seen that at fuel price set to 0.2 and 0.5 $/liter, the seawater reverses osmosis desalination/photovoltaic/diesel generator/battery is the most cost-effective energy system, and when fuel price is 1 $/liter, the seawater reverses osmosis desalination/photovoltaic/wind turbine/diesel generator/battery is the most cost-effective hybrid system. While at fuel price set to 0.2, 0.5, and 1 $/liter, the seawater reverse osmosis desalination /photovoltaic/wind turbine/diesel generator/battery is the most eco-friendly. Finally, the results of this study show proposed algorithm is faster and more accurate (100 iterations, 98.36% accuracy) than the genetic algorithm (1000 iterations, 83.03% accuracy) and the artificial bee swarm optimization (300 iterations, 95.49% accuracy). [ABSTRACT FROM AUTHOR]

• The optimal allocation of PV and DSTATCOM. • Cost minimization and net saving maximization. • Applying the two recent optimization algorithms for solving the studied optimization problem. • Determining the candidate losses based on sensitivity analysis. • Studying the annual total cost reduction at different penetration levels. This paper introduces an efficient two-stage procedure for assigning the optimal sites and sizes of the photovoltaic-based distributed generator (PV-DG) and distribution static synchronous compensator (DSTATCOM). This procedure is applied for radial distribution network (RDN) under seasonal variations of irradiance and load demand. In the first stage, the voltage loss sensitivity index (VLSI) is utilized to identify the high potential buses for integration of the PV-DG and DSTATCOM while in the second stage, two recent optimization algorithms including the lightning attachment procedure optimization (LAPO) and the equilibrium optimizer (EO) are utilized to identify the best sizes and locations of the PV-DG and DSTATCOM from the candidate nodes. The proposed algorithms are tested on a 118-bus distribution system and the obtained results are compared with the Whale optimization algorithm (WOA) and sine cosine algorithm (SCA). The computational study shows the superiority of the application of the new optimization techniques (LAPO and EO) compared with WOA and SCA. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: A triple-objective design method was developed for a solar chimney power plant system that simultaneously optimizes the expenditure, total efficiency, and power output. A multi-objective genetic algorithm was used to obtain the best combination of geometric parameters of the power plant. The following design parameters were selected: collector radius, chimney height, and chimney diameter. Two different solar chimney power plant configurations were considered: the Kerman pilot power plant and Manzanares prototype power plant. A set of possible optimal solutions (Pareto optimal set) was obtained. Based on the optimal solutions, the best configuration for each power plant was selected. The performance and expenditure of the optimal solutions and the built power plants were compared. The results showed that the increment of the power output was higher than the increment of the expenditure in the optimal configuration. A parametric study was conducted to evaluate the effects of changing design parameters on different objective functions. This paper provides a very useful design and optimization methodology for solar chimney power plant systems. [Copyright &y& Elsevier]

Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the literature for sizing nanogrid systems due to simplicity and speed. However, the method has limited capabilities in implementing multi-configurational analysis and requires large formulations. In this paper, nested integer linear programming is proposed to decompose the large formulations and simplify the multi-configurational sizing of residential nanogrid in a semiarid zone. The proposed method is aimed at optimal sizing for energy cost reduction and increased supply availability. The method is implemented in multi-stage hybridization of relaxation and integer methods of linear programming to achieve optimal sizes of the nanogrid components using photovoltaics, wind turbines, and battery. The method realizes $72,343 net present cost and 0.3755 $/kWh levelized cost of energy indicating 33% and 11% reductions compared to mixed integer linear programming and particle swarm optimization. System availability of 99.97% is envisaged to achieve 6677–7782 kWh per capita electricity consumption in residential buildings against the existing 150 kWh. Three configurations analyzed indicated the robustness of the proposed method and the multi-configurational designs clarify options against factors such as space, logistics, or policies. • Nested integer linear programming algorithm is proposed for nanogrid sizing. • The method improved the reference algorithm through multi-configurational designs. • Levelized costs of energy were reduced by 7.7–39.7% in the three configurations. • The net present costs were reduced by 11.2–46.4% in two configurations. • The method optimized the nanogrid's capacity by 54.8–68.1%. [ABSTRACT FROM AUTHOR]

Several countries are adopting plans to reduce the contaminant emissions from the energy sector through renewable energy integration and restrictions on fossil fuel generation. This process poses important computational and methodological challenges on expansion planning modeling due to the operational details needed for a proper analysis. In this context, this paper develops a planning model including an effective representation of the operational aspects of the system to understand the key role of flexible resources under strong decarbonization processes in highly renewable power systems. A case study is developed for the Chilean power system, which is currently undergoing an ambitious coal phase-out process, including the analysis of a scenario that leads to a completely renewable generation mix. The results show that highly renewable generation mixes are feasible, but rely on an effective balance of the key flexibility attributes of the system including ramping, storage, and transmission capacities. Further, such balance allows for faster decarbonization goals to remain in a similar cost range, through the deployment of flexible capacity in earlier stages of the planning horizon. • Flexibility is a critical challenge for the decarbonization of power systems. • Planning models with operational detail capture key trends through decarbonization. • A planning model with a linear relaxation of the unit commitment problem is proposed. • A case study on decarbonization pathways for the Chilean power system is presented. • Fully renewable mixes are feasible through a proper balance of flexibility attributes. [ABSTRACT FROM AUTHOR]

Abstract: Forest biomass is one of the renewable sources of energy that has been used for generating electricity. The feasibility and cost of producing electricity from forest biomass depend on long term availability of biomass, its cost and quality, and the cost of collecting, pre-processing, handling, transportation, and storage of forest biomass, in addition to the operating and maintenance costs of the conversion facility. To improve the cost competitiveness of forest biomass for electricity generation, mathematical programming models can be used to manage and optimize its supply chain. In this paper, the supply chain configuration of a typical forest biomass power plant is presented and a dynamic optimization model is developed to maximize the overall value of the supply chain. The model considers biomass procurement, storage, energy production and ash management in an integrated framework at the tactical level. The developed model is a nonlinear mixed integer programming which is solved using the outer approximation algorithm provided in AIMMS software package. It is then applied to optimize the supply chain of a real biomass power plant in Canada. The optimum solution provides more profit compared to the actual profit of the power plant. Different scenarios for maximum available supply and also investment in a new ash recovery system were evaluated and the results were analyzed. The model in particular shows that investment in a new ash recovery system has economic as well as environmental benefits for the power plant. [Copyright &y& Elsevier]