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Developing photovoltaic (PV) and wind power is one of the most efficient approaches to reduce carbon emissions. Accumulating the PV and wind energy resources at different geographical locations can minimize total power output variance as injected into the power systems. To some extent, a low degree of the variance amplitude of the renewable resources can reduce the requirement of in-depth regulation and dispatch for the fossil fuel-based thermal power plants. Such an issue can alternatively reduce carbon emissions. Thus, the correlation problem by minimizing the variance of total PV and wind power plays a vital role in power system planning and operation. However, the synergistic effect of power output correlation is mainly considered on the generation side, and it is often neglected for the correlation relationship between the power grid components. To address this problem, this paper proposes a correlation coefficient analysis method for the power grid, which can quantify the relationship between energy storage and the probabilistic power flow (PPF) of the grid. Subsequently, to accelerate the mapping efficiency of power correlation coefficients, a novel deep neural network (DNN) optimized by multi-task learning and attention mechanism (MA-DNN) is developed to predict power flow fluctuations. Finally, the simulation results show that in IEEE 9-bus and IEEE14-bus systems, the strong correlation grouping percentage between the power correlation coefficients and power flow fluctuations reached 92% and 51%, respectively. The percentages of groups indicating weak correlation are 4% and 38%. In the modified IEEE 23-bus system, the computational accuracy of MA-DNN is improved by 37.35% compared to the PPF based on Latin hypercube sampling. Additionally, the MA-DNN regression prediction model exhibits a substantial improvement in assessing power flow fluctuations in the power grid, achieving a speed enhancement of 758.85 times compared to the conventional probability power flow algorithms. These findings provide the rapid selection of the grid access point with the minimum power flow fluctuations. [ABSTRACT FROM AUTHOR]

The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by ≤33.3% with a 1.5 times increase in the load demand and by ≤50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by ≤80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs. [ABSTRACT FROM AUTHOR]

In order to deal with the increasingly severe energy shortage problem, to find new energy solutions and to alleviate the dual pressure of domestic energy and environment, building a new type of multi-energy complementary power system has become one of the hot issues in recent years. This paper presents an optimal scheduling model of building integrated energy system (BIES) based on the comprehensive utilization of electricity, heat and gas. Based on the traditional combined cooling, heating, and power (CCHP) system, this model adds wind power and photoelectric renewable energy power generation systems, ground source heat pump system and energy storage system. Then, taking the environmental cost and system operation and maintenance cost as the objective function, it is solved by using the improved multi-objective ant lion algorithm based on differential evolution. Finally, a building in Northeast China is taken as an example to verify the simulation. The simulation results show that the optimal scheduling model of building integrated energy system proposed in this paper can promote the consumption of renewable energy such as wind power and photoelectric, realize the peak cutting and valley filling of power load, effectively reduce the economic operation cost of the system, reduce carbon emissions and effectively solve the problem of environmental pollution to a certain extent. [ABSTRACT FROM AUTHOR]

Introduction: Distributed power supply has increasingly taken over as the energy industry's primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources. Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master-slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster. Results: Simulation results show that the proposed framework has better performance under different operating conditions. Discussion: The energy storage operators and numerous energy storage users can implement master-slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win-win conditions. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy generation systems increases, traditional power generation facilities begin to face challenges, such as reduced output power and having the power turned off. The challenges are causing changes in the structure of the power system. Renewable energy sources, mainly wind and solar energy cannot provide stable inertia and frequency regulation capability. Ultimately, the power system's emergency response capability to face an N-1 is reduced, which leads to a reduction in system stability. Therefore, the application technology of the battery energy storage system is used to support the impact of changes in the new power system structure. This paper designed control technologies based on the WECC second-generation generic model, namely, dynamic regulation, steady regulation, and virtual inertia regulation. The models and control strategies are verified on Taiwan's 2025 power system target conditions, which consider the expected capacities for battery energy storage systems, and renewable energy sources with different load and N-1 fault levels. According to the simulation results, the capabilities of the RoCoF limitation, frequency nadir, frequency recovery, and system oscillation regulation are evaluated in the proposed strategies. Finally, the analysis results can help power operators make informed decisions when selecting and deploying battery energy storage systems. [ABSTRACT FROM AUTHOR]

In this paper, stochastic synchronization of the wind and solar energy using energy storage system based on real-time pricing in the day-ahead market along with taking advantage of the potential of demand response programming has been analyzed. Since renewable energies, loads and prices are uncertain, and planning is based on real-time pricing, the optimal biding proposition considers the wind power, solar system, and energy storage system. Uncertainty is addressed to solve the bidding strategy in a day-ahead market for optimal wind and PV power and optimal charging for energy storage. Batteries are the most promising device to compensate for the fluctuations of wind and photovoltaic power plants to mitigate their uncertainty. In general, using MILP is a suitable approach to address uncertainty as long as a linear formulation is acceptable for modeling either with continuous variables or integer ones. By setting some scenarios to formulate market prices, imbalance of energy, wind and solar system, the uncertainty problems could be easily solved by MILP solver. The model created enables the retailer to realize the potentials of the demand response program and exploit high technical and economic advantages. To ensure fair prices, a set of regulating constraints is considered for sales prices imposed by the regulation committees. A model is presented to optimize the electricity trading strategy in the electricity market, considering the uncertainty in the wholesale market price and the demand level. The retailer considered in this paper is a distribution company that is the owner and operator of the networks and operates under real-time pricing regulations. To model demand response, the elasticity coefficient is used. The proposed solution is implemented on a standard 144-bus sample network using a nonlinear integer programming method. The presented method results provide helpful and valuable information based on the optimal method proposed by the retailers considering the demand response program and real-time pricing system. [ABSTRACT FROM AUTHOR]

This paper presents a techno-economic analysis of 'Picogrid' in hilly remote areas of North-East India, where availability of conventional grid power is either none or limited. A Picogrid is a small microgrid (typically a few tens of kW) containing renewable energy sources like solar, wind, small hydro etc. with battery based energy storage of limited capacity. A hybrid solar PV-Hydro based Picogrid of 7.2 kW capacity in a remote hilly area is analyzed, where the solar irradiance varies between 3.5 and 6.2 kWh/Day/m2, a water fall head lies between 1 and 30 m and water flow rate varies from 100 to 50,000 L/h. The study reveals that, the annual total energy generation varies between 1200 and 5000 kWh/kW/Year. Whereas the annual average daily yield and Capacity Utilization Factors for such system are 9 kWh/kWp/Day and 40%, respectively at highest possible water head and flow rate as mentioned above. The optimized Picogrid system is shown to have a levelised cost of electricity of 1.4 INR (0.017 USD) and 1.8 INR (0.022 USD) per kWh for 100% and 30% equity investment, respectively. The energy produced by these methods can be provided in rural areas thereby making electricity and revenue producing probabilities concurrently. [ABSTRACT FROM AUTHOR]

The use of renewable energy for desalination started in the 1980s, in order to provide a sustainable water supply in windy/sunny areas with water shortages. Nevertheless, this initiative has been generally limited to the R&D field and prototypes, with few units operating under real conditions. The research tradition in this field carried out by the Canary Islands Institute of Technology, based on pilot facilities, resulted in wide expertise on practical issues, as well as a deep knowledge on the state of the art. This paper deals with the most relevant technical aspects to be considered in the optimal design and operation of wind/photovoltaic (PV)-powered reverse-osmosis (RO) systems, focusing on practical indications: appropriate pre-treatment, the use and selection of the RO energy recovery system (ERD), the selection of an energy storage system, key recommendations for the O&M actions in wind- and PV-powered RO systems (extracted from the experience of operating different units in remote locations (PV and RO) and coastal areas (wind and RO)), and an identification of the pros and cons of hybrid systems. A selection of economic data is given, indicating the main aspects of the minimization of the cost of water. Finally, the paper mentions the latest advances in the involved technologies. [ABSTRACT FROM AUTHOR]

With the strong advancement of the global carbon reduction strategy and the rapid development of renewable energy, compressed air energy storage (CAES) technology has received more and more attention for its key role in large-scale renewable energy access. This paper summarizes the coupling systems of CAES and wind, solar, and biomass energies from the perspective of system topology, and points out the advantages and limitations of each system. It is shown that the coupling of wind energy and CAES is mainly combined in series and in parallel, and sometimes part of the wind power can be converted into thermal energy when coupled to CAES. The coupling between solar heat and CAES is an important form of coupling between solar energy and CAES. Solar-heat-coupled CAES mainly uses solar energy to heat expander inlet air. The coupling forms of solar energy and CAES are based on various CAES forms, various heat heating sequences, reheating, bottom cycle, and other factors. The combination of biomass and CAES is generally based on biomass gasification power generation technology. In the future, a wind–solar–CAES multiple coupling system is expected to become a promising large-scale form for the utilization of renewable energy, and this integrated system has great potential as a system configuration, but has some technical challenges. [ABSTRACT FROM AUTHOR]

This paper presents an off-grid sustainable power generation system suitable for rural areas of developing countries such as India where open wells are available for the purpose of irrigation and meeting the daily requirements of local families. The proposed scheme comprises of a small wind energy system with pumped-hydro energy storage (PHES), which uses an open well as a source of water. The proposed system is different from all available wind-PHES systems in many ways. It uses two upper reservoirs instead of one; the hydraulic pump gets rotational energy directly from the wind turbine, while the pico-hydro turbine coupled with electric generator gets power from the potential energy of water stored in the upper reservoir. The mathematical model of the system has been developed and the actual wind speed data are used to carry out simulation for evaluation of the model performance. The attractive features of the scheme are its reliability, simplicity, and capability to supply continuous power at constant voltage exactly equal to the rated one in spite of wind speed variations. Another interesting use of the proposed system is to supply water to the locals along with electric power supply. [ABSTRACT FROM AUTHOR]

In the context of electricity generation in the oil and gas producing countries, it has become implicit to develop new, clean, and renewable sources of energy to supplement the fossil fuel based power generation. With this single aim, one can hit two targets, conservation of fixed fossil fuel resources and combat the adverse climatic changes to safeguard our planet. In regional context, solar photovoltaic, solar thermal, wind power, geothermal, and hydro power are alternative sources for power mitigation. Of these renewables, wind, solar photovoltaic (PV), diesel, and energy storage in hybrid combinations are the possible ways to supply continuous energy for all sizes of applications. This paper provides a review of the existing hybrid power systems and the theoretical studies around the globe in varied climatological conditions to identify the best combinations of hybrid systems for Saudi Arabia to supplement the existing energy portfolio. The wind/solar-pv, wind/solar-pv/diesel, and solar-pv/diesel with and without battery backup are most commonly used systems with respective popularity of 28, 22, and 21%. Among users, remote communities are the highest while islands and communication towers are the next common applications. Average costs of energy of wind/solar-pv, wind/solar-pv/diesel, and solar-pv/diesel are around 0.458, 0.355, and 0.349 US$/kWh. [ABSTRACT FROM AUTHOR]

Renewable energy attracts increasing attention from both industry and academia under the context of carbon neutrality. For wind and solar energy, the strong dependence on natural processes results in the imbalance between energy production and real demands. Energy storage technologies, e.g., Compressed Air Energy Storage (CAES), are promising solutions to increase the renewable energy penetration. However, the CAES system is a multi-component structure with multiple energy forms involved in the process subject to high temperature and high-pressure working conditions. The CAES system is a complex process flowsheet consisting of charging and discharging process. The process should be optimized to achieve the best thermodynamic and economic performance. Under the optimal design conditions, it might lead to severe consequences once a failure occurs, e.g., harm to humans, the environment, and assets. Limited attention and scarce available information have been paid to the CAES system risk management yet. Hence, this paper applies the System-Theoretic Process Analysis (STPA), which is a top-down method based on system theory, to identify the CAES system safety hazards. The results are expected to provide a preliminary guideline for practitioners regarding the safety and reliability of the CAES system. As a result, a more reliable CAES system can contribute to a more flexible energy system with more efficient and economic utilization of fluctuating renewable energy. [ABSTRACT FROM AUTHOR]

In the renewable energy consumption scheduling, due to the large fluctuation of wind power output, the renewable energy consumption rate is low, and the energy consumption scheduling effect is poor. Therefore, a new renewable energy consumption scheduling model based on quantitative feedback theory is designed. For the first time, calculate the output response of wind power generation, obtain the output change rate of the wind farm at the time scale, and determine the response curve of wind power generation according to the power generation process is divided into three stages: the initial stage, the peak stage and the end stage, and the response output during the peak period, so as to obtain the transfer rate of output during the peak period. Build a mathematical model of renewable energy power generation, calculate the output state of wind power generation, and analyze the output characteristics of renewable energy storage system; Secondly, the objective weight coefficient of renewable energy consumption is determined by using quantitative feedback theory, the objective function of renewable energy consumption is constructed, the power injected by parameter nodes is determined, the quantitative feedback controller is designed by using loop shaping technology, the control structure of two degrees of freedom is determined, the objective function of renewable energy consumption is constructed, and the constraint range of renewable energy consumption capacity is determined according to static constraints such as current constraints and voltage constraints; Then, the quantitative feedback theoretical controller is designed, the input and output transfer functions of the consumption system are determined, and the renewable energy consumption scheduling model is constructed. The renewable energy consumption scheduling model is solved by particle swarm optimization through a variety of index parameters in the renewable energy consumption determined by the QFT controller. The experimental results show that the proposed model can effectively improve the renewable energy consumption rate and optimize the consumption scheduling effect. [ABSTRACT FROM AUTHOR]

Sources such as solar and wind energy are intermittent, and this is seen as a barrier to their wide utilization. The increasing grid integration of intermittent renewable energy sources generation significantly changes the scenario of distribution grid operations. Such operational challenges are minimized by the incorporation of the energy storage system, which plays an important role in improving the stability and the reliability of the grid. This study provides the review of the state-of-the-art in the literature on the economic analysis of battery energy storage systems. The paper makes evident the growing interest of batteries as energy storage systems to improve techno-economic viability of renewable energy systems; provides a comprehensive overview of key methodological possibilities for researchers interested in economic analysis of battery energy storage systems; indicates the need to use adequate economic indicators for investment decisions; and identifies key research topics of the analyzed literature: (i) photovoltaic systems with battery energy storage systems for residential areas, (ii) comparison between energy storage technologies, (iii) power quality improvement. The last key contribution is the proposed research agenda. [ABSTRACT FROM AUTHOR]

Summary: Alternative sources of electricity have been growing recently despite their intermittence, which makes it impossible for these sources to guarantee constant and uninterrupted supply of electricity. Energy storage systems, such as pumped‐storage (PS) power plants, can help to mitigate the intermittence of these sources. In Brazil, intense growth of intermittent sources has led the generation sector to high exposure to the spot market. Thus, Brazilian electricity sector has recently turned its attention to energy storage methods as a way of diminishing such exposure. However, Brazilian electricity‐sector regulatory framework is not prepared for energy storage systems remuneration. This paper proposes a regulatory framework to insert hybrid electric generation units composed of PS and intermittent sources in Brazilian energy market. A linear optimization model exemplifies the operation of such system by minimizing costs for 1 day. The model was tested in three different scenarios, allowing comparison of operational costs and other aspects of the operation. The results demonstrate how a PS can help covering peak demand by storing energy to be used at moments when generation from intermittent sources is not sufficient. In the proposed regulatory framework for hybrid plants, expenses with spot market purchases can be diminished either by completely avoiding it or by purchasing only during lower demand level periods. [ABSTRACT FROM AUTHOR]

This paper proposes a multisource multiproduct framework for coupled multicarrier energy supplies with a biogas–solar–wind hybrid renewable system. In this framework, the biogas–solar–wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic interactions of electricity, gas, and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multienergy complementarity of biogas–solar–wind renewable portfolio can be utilized to facilitate the mitigation of renewable intermittency and the efficient utilization of batteries, and a multicarrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient conversion and storage, while different energy demands of end-users are satisfied. The proposed methodology has been fully tested and benchmarked on a stand-alone Microgrid over a 24-h scheduling horizon. Comparative results demonstrate that the proposed scheme can lower the battery charging/discharging actions as well as the degradation cost, and also confirm its capability to accommodate high penetration of variable renewables. [ABSTRACT FROM AUTHOR]

Electrical energy becomes necessary for human being. Generation of electrical energy mostly depends on fossils fuel, they are limited in nature and also responsible for environmental pollution. Renewable energy resources provides a better alternative for future,In comparison to conventional energy resources economical aspect is a major issue of renewable energy sources with the feasibility and efficiency. These limitations are tried to overcome by deployment hybrid renewable energy resources. There are certain criteria to analyze and implement the sized, optimized and cost efficient system. This paper focus on hybrid energy systems based on solar photovoltaic (PV) and wind resources. This paper shed lights on various parameters of economic feasibility, sizing strategies with logical advancements to enhance their utilization, future prospects, and their arrangement. Strategies to develop an effective storage system is also presented here.A brief review on developments in optimization techniques, reliability index and cost analyzing techniques for hybrid renewable energy systems are also presented. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to exploit the wind potential of the Danube cluster by storing it in the Iron Gates I reservoir lake, through pumping the water downstream of the hydroelectric power plant. Storing energy by pumping water, i.e. turning the plant into a pumped storage hydroelectric plant, will chancel the unstable character of wind energy, also achieving peak load coverage under conditions of investment much be-lower to those of building a hydroelectric power plant with accumulation by pumping the Danube. [ABSTRACT FROM AUTHOR]

Prince Edward Island, already with the highest wind energy penetration of any province in Canada, aims to increase its renewable energy generation. A challenge is realizing better matching of intermittent wind and solar power with on-island electricity demand. This paper provides an initial perspective by considering the case of a 100% renewable island electricity system. Using time-series power data of wind, solar, and electricity demand for the province, varying capacities of wind and solar power along with battery energy storage and biomass are considered to model 100% renewable electrical energy scenarios. Wind, solar, and storage combinations without curtailment show how complementary supply combinations can reduce seasonal variations. Scenarios including curtailment demonstrate significantly reduced storage requirements. Scenarios that include biomass suggest little benefit in combining biomass and storage. Least-cost solar, wind, and storage capacities are estimated. Potential storage cost reductions appear to be transformative in enabling greater renewable energy integration. [ABSTRACT FROM AUTHOR]

• A comprehensive operational cost function was established based on the deep peak shaving characteristics of thermal generators. • The impacts of different combinations of renewable energy penetration on the power system operation were analyzed. • The effect of energy storage deployment on improving flexibility in high penetration renewable energy systems were analyzed. Higher proportions of renewable energy are one of the most prominent features of future power systems. However, renewable energy, such as wind and PV power generation, is subject to inherent variability and uncertainty, which leads to fluctuations in electrical production and necessities flexibility enhancement to ensure sufficient adjustable resources responding to such fluctuations. This paper proposes to enhance the flexibility of renewable-penetrated power systems by coordinating energy storage deployment and deep peak regulation of existing thermal generators. First, the growing flexibility requirement in the presence of variable renewable energy is discussed and quantified using proposed indices. Then, we analyze the characteristics of thermal generators providing deep peak regulation, and establish a comprehensive operation cost function including coal consumption, wear-and-tear consumption, and oil consumption of thermal generators. On this basis, we propose a flexibility enhancement method coordinating battery energy storage capacity optimization and deep peak regulation of thermal generators, which aims at minimizing the total investment and operation costs while satisfying operating constraints on representative days. Extensive case studies on a modified IEEE-RST 24 system verified the proposed flexibility enhancement method under different scenarios. [ABSTRACT FROM AUTHOR]

In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models; (2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ); (3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions. [ABSTRACT FROM AUTHOR]

The growth in renewable power generation and more strict local regulations regarding power quality indices will make it necessary to use energy storage systems with renewable power plants in the near future. The capacity of storage systems can be determined using different methods most of which can be divided into either deterministic or stochastic. Deterministic methods are often complicated with numerous parameters and complex models for long term prediction often incorporating meteorological data. Stochastic methods use statistics for ESS (Energy Storage System) sizing, which is somewhat intuitive for dealing with the random element of wind speed variation. The proposed method in this paper performs stabilization of output power at one minute intervals to reduce the negative influence of the wind farm on the power grid in order to meet local regulations. This paper shows the process of sizing the ESS for two selected wind farms, based on their levels of variation in generated power and also, for each, how the negative influences on the power grid in the form of voltage variation and a shortterm flicker factor are decreased. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present options to make low-carbon footprint large-scale desalination a reality on arid islands with weak electrical grids. Through these options, the goal is to reconfigure on-grid wind energy/desalination systems for large- and medium-scale water production. In this context, it is proposed to use lithium-ion batteries for stationary energy storage together with management strategies aimed at avoiding the wind energy/desalination systems having to consume energy from the conventional grid they are connected to. The control strategy is based on ensuring that the power provided by the wind farm and batteries remains in synchrony with the power demand of the desalination plant throughout the system's useful life. The interannual variation of wind energy is considered when sizing the renewable energy system and processes for its estimation are proposed. The case study is centred on the Canary Archipelago, a region that is especially vulnerable to the impacts of climate change, but which enjoys exceptional characteristics for the exploitation of wind energy. The results obtained show the optimal wind farm and energy storage system capacities of the analysed configurations. The approach presented allows a low-carbon operational footprint. If the control strategy were to be put into practice today, the current grid restrictions and a life cycle assessment of the system carried out in a societal context that continues to be fossil fuel dependent indicate a potential reduction of 77.4% of the footprint. However, the remaining 22.6% could be eliminated in the future when the manufacturing processes of wind turbines, batteries and desalination plants receive the benefits of carbon-neutral societies. [Display omitted] • A method for achieving large-scale, low-carbon desalination is presented. • Optimal wind farm and storage capacities for the system are determined. • The method offers substantial CO2 emission reduction potential. • Using climate ERA5 reanalysis data, enhances robustness by addressing wind variability. • Proposed adjustments enhance adaptability to technological advances and future scenarios. [ABSTRACT FROM AUTHOR]

The concept of off-grid hybrid wind energy system is financially attractive and more reliable than stand-alone power systems since it is based on more than one electricity generation source. One of the most expensive components in a stand-alone wind-power system is the energy storage system as very often it is oversized to increase system autonomy. In this work, we consider a hybrid system which consists of wind turbines, photovoltaic panels, diesel generator and battery storage. One of the main challenges experienced by project managers is the sizing of components for different sites. This challenge is due to the variability of the renewable energy resource and the load demand for different sites. This paper introduces a sizing model that has been developed and implemented as a graphical user interface, which predicts the optimum configuration of a hybrid system. In particular, this paper focuses on seeking the optimal size of the batteries and the diesel generator usage. Both of these components are seen to be trade-offs from each other. The model simulates real time operation of the hybrid system, using the annual measured hourly wind speed and solar irradiation. The benefit of using time series approach is that it reflects a more realistic situation; here, the peaks and troughs of the renewable energy resource are a central part of the sizing model. Finally, load sensitivity and hybrid system performance analysis are demonstrated. [ABSTRACT FROM AUTHOR]

This study proposes an integrated energy system (IES) model consisting of natural gas system, electricity system, and power-to-gas stations (P2GSes), and then uses a centralised coordinated method to plan the IES. P2GSes can convert excessive renewable energy to natural gas and provide a promising approach to large-scale energy storage and long-distance energy transmission in the form of gas. However, considering the carbon emission of natural gas utilisation and high cost of P2G process, new embedded P2GSes will pose a threat to the economic and environment-friendly operation for the existing integrated energy system. Therefore, a more comprehensive P2G revenue objective, which is comprised of a secondary reserve service model, wind power integration model and carbon trading market participating model, is proposed in this study. The potential of P2GSes to provide the capacity of absorbing wind power and carbon reduction is innovatively and thoroughly evaluated in this study. The effectiveness of the proposed model is verified on an IES containing a coupled 24-bus electricity and 20-bus natural gas system. Simulation results show that it is optimal for the P2GSes revenue model to possess both capacities of absorbing wind power and converting the acquired power into natural gas as much as possible. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time. [ABSTRACT FROM AUTHOR]

Building a multi-energy complementary power generation system is a viable way to encourage the use of renewable energy and decarbonize power generation. However, the intermittent nature of renewable power generation, such as photovoltaic and wind power, has prompted concerns regarding power grid stability. To balance such fluctuations, energy storage systems or other flexible power generation technologies should be integrated. In this paper, the peak regulation ability of integrated solar combined-cycle has been enhanced via employing a gas/oil exchanger between the top and bottom cycle. When integrating high penetration intermittent renewable energy, an appropriate operational strategy towards high-quality steady power output regulation is proposed. Dynamic performance analysis of the system, coupled characteristic of heat and mass transfer between subsystems have been highlighted. The case study demonstrates that fluctuations of the multi-energy complementary system power output can be controlled below 0.3 MW without renewable energy curtailment, even though wind-PV power generation fluctuates from 0.3 MW to 26.1 MW with variations per second reaching −2.8/3.7 MW. Furthermore, the system's levelized cost of electricity is down to 0.0512 $/kWh, which is cost-competitive with conventional power generation technologies. • Combination of integrated solar combined cycle with wind and PV power plant. • Power stability of system with high penetration intermittent energy. • Novel system configuration and operation strategy. • Deviations from target power output below 0.3 MW per second. [ABSTRACT FROM AUTHOR]

Energy systems (ES) are seriously affected by climate variability since energy demand and supply are dependent on atmospheric conditions at several time scales and by the impact of severe extreme weather events (EWEs). EWEs affect ES and can cause partial or total blackouts due to energy supply disruptions. These events significantly impact essential infrastructures and are considered one of the main causes of wide-area electrical disturbances worldwide. A comprehensive review is carried out based on 210 published studies using searches from Scopus and Google Scholar databases, to assess the impacts of EWEs—such as extreme storms, wind, and lightning events, heat, or cold waves, and freezing—on ES and their associated infrastructures—production, transmission, and distribution—worldwide, with a particular focus on wind energy systems (WES). Strategies and measures are critically reviewed and synthesized to minimize and mitigate the impact of EWEs, protect, and adapt the systems to maintain regular operations even when these events occur. Finally, physical modifications to systems and the incorporation of new technological solutions such as energy storage systems (ESS), distributed energy systems (DES), and microgrids, can enhance the network resilience and mitigate the EWEs effects. Highlights: Extreme weather events (EWEs) have a major impact on energy systems (ES). Wind energy systems are particularly affected by EWEs. Measures and new solutions are needed to minimize the impacts of EWEs on the ES. ES need to be adapted to maintain normal operation during the occurrence of EWEs. EWEs are now a great challenge to researchers and engineers. [ABSTRACT FROM AUTHOR]

The transition from traditional energy sources to renewable has gained popularity and acceptance in recent years. This has been driven mainly by the current level of pollution, global warming, decommission of old nuclear power plants and the increasing cost of conventional energy sources. Nevertheless, one of the many steps to overcome is the seasonality or intermittency of renewable energy sources such as wind power. In recent years, new technologies have come up to address this problem, so the energy can be stored for future purposes. This paper analyses these energy storage alternatives for a specific case in the Columbia River Gorge for wind power. A hierarchical decision model is developed with criteria including political, social, environmental, technical and economical. The main conclusions highlight that the economical and technical criteria are among the most important ones for decision-makers. [ABSTRACT FROM PUBLISHER]

• FuturES framework optimises electricity scenarios with high renewables penetration. • Reservoir and concentrating solar power modelled based on storage value. • Feasible scenarios created with 81–100% renewables by 2050. • Long-term energy storage helps to achieve low electricity costs. Although electricity supply is still dominated by fossil fuels, it is expected that renewable sources will have a much larger contribution in the future due to the need to mitigate climate change. Therefore, this paper presents a new framework for developing Future Electricity Scenarios (FuturES) with high penetration of renewables. A multi-period linear programming model has been created for power-system expansion planning. This has been coupled with an economic dispatch model, PowerGAMA, to evaluate the technical and economic feasibility of the developed scenarios while matching supply and demand. Application of FuturES is demonstrated through the case of Chile which has ambitious plans to supply electricity using only renewable sources. Four cost-optimal scenarios have been developed for the year 2050 using FuturES: two Business as usual (BAU) and two Renewable electricity (RE) scenarios. The BAU scenarios are unconstrained in terms of the technology type and can include all 11 options considered. The RE scenarios aim to have only renewables in the mix, including storage. The results show that both BAU scenarios have a levelised cost of electricity (LCOE) lower than, or equal to, today's costs ($72.7–77.3 vs $77.6/MWh) and include 81–90% of renewables. The RE scenarios are slightly more expensive than today's costs ($81–87/MWh). The cumulative investment for the BAU scenarios is $123-$145 bn, compared to $147-$157 bn for the RE. The annual investment across the scenarios is estimated at $4.0 ± 0.4 bn. Both RE scenarios show sufficient flexibility in matching supply and demand, despite solar photovoltaics and wind power contributing around half of the total supply. Therefore, the FuturES framework is a powerful tool for aiding the design of cost-efficient power systems with high penetration of renewables. [ABSTRACT FROM AUTHOR]

Highlights • A model of district heating network is established in detail. • Utilization of the energy storage characteristics of DHN to reduce wind curtailment. • We establish a thermal and electrical coordination stochastic optimal dispatch model. • We use Benders decomposition algorithm to solve the model. • The energy storage characteristics of the DHN is quantitatively analyzed. Abstract The strong coupling between heating supply and power generation in heat and power (CHP) units limits their consumption of wind power and lacks the necessary flexibility. Based on the theory of heat transfer, this paper considers the temperature loss and energy storage characteristics on the transmission side to establish a stochastic optimal dispatch model of considering thermal and electrical coordination. Utilization of the energy storage characteristics of the district heating network (DHN) to reduce the wind power curtailment and accommodate variable wind power. Benders decomposition algorithm is used to solve the model and improve the efficiency of the solution. Finally, the effectiveness of the proposed model and algorithm was verified by a thermal/electrical system example, and the energy storage characteristics of the DHN were quantitatively analyzed. [ABSTRACT FROM AUTHOR]

Abstract A number of valid possible arrangements of renewable energy sources (wind turbines, solar photovoltaics) with energy storage systems (electrochemical storage, fuel cell, battery) for the large-scale electric grid system (26 GW) are explored in this paper. There are two core motivations for choosing such arrangements. First, the same type of renewable resources located at a single site have high fluctuation, so we model various type of renewable resources located at various locations to have lesser fluctuations accompanied with relatively compact energy storage systems. The second incentive is to find the true minimum cost combination, excluding subsidies and relaxation in taxes for governments. This study includes the whole year (hourly, 8760 h in total) electric load and related weather elements. It is found that the least cost combinations require excessive generation capacity, diverse renewable generation resources and energy storage techniques, which would meet the load requirements and have less carbon emissions. Graphical abstract Image 1 Highlights • We modeled renewables & storage to meet demand of large-scale electric grid (26 GW). • We investigated several combinations of RES & storage for a large grid. • Economical combinations have excessive generation (1.3–2 times of load). • Economical combinations with 80% of RES penetration have 220% of installed capacity. • Economical combinations with 93% of RES penetration have 312% of installed capacity. [ABSTRACT FROM AUTHOR]

A scheme that allows the dispatch of steady and controllable level of power from a wind power generating station is proposed in this paper. The scheme utilizes two battery energy storage systems (BESSs) in which the generated wind power is used to charge one BESS, while the second BESS is used to discharge constant power into grid. The role of the two BESS interchanges when the discharging BESS reaches specified operating limit. With this scheme in mind and based on given wind speed statistics, charging characteristics of the BESS are studied, and a method to determine the expected charging time of the BESS to reach stipulated battery state of charge is developed. The expected BESS charging time, in turn, dictates the constant power level that can be dispatched to the grid through the discharging BESS. The corresponding discharge time is also determined using the developed method, the accuracy of which is validated experimentally. The proposed design procedure is then used to determine the minimum BESS capacity based on the expected wind power. Statistical likelihood of dispatchable power delivery achievable from the scheme is also obtained. [ABSTRACT FROM AUTHOR]

To alleviate the issues of climate warming and fossil resources depletion, this paper proposes a wind-photovoltaic-thermal energy storage-electric heater cogeneration system, which adopts the structure of concentrated solar power plant but replaces the expensive collector field and tower with wind and photovoltaic farms and electric heater that converts the excess electricity as thermal energy source. The cogeneration system can effectively regulate the fluctuating wind and photovoltaic output to satisfy electricity load by electric heater based on resistive heating of molten salt, two-tank molten salt thermal energy storage and power block based on steam Rankine cycle, and supply heat load directly from the molten salt heat transfer fluid and heat exchanger. This paper considers minimizing levelized cost of energy, loss of power and heat supply probability, energy reduction rate and lifecycle equivalent CO 2 emissions as optimized objectives, and investigates the many-objective (having four or more objectives) capacity optimization problem by Nondominated Sorting Genetic Algorithm-Ⅲ coupled with Principal Component Analysis and Technique for order preference by similarity to ideal solution. Moreover, residual analysis is applied to consider the uncertainty of wind power prediction. The detailed optimization process and performance comparison of many-objective algorithms, the all-year energy flow and seasonal output characteristics analysis of the optimal-compromise solution are presented. Finally, the proposed cogeneration system is compared to the power-only system with the same installed capacity and equivalent load demand, and the comparison results show that the proposed cogeneration system performs better in all evaluation metrics. Therefore, the proposed renewable energy cogeneration system is feasible and cost-effective for practical applications. • A novel wind-photovoltaic-thermal energy storage cogeneration system is proposed. • A many-objective capacity optimization model with uncertainty is developed. • Economy, reliability, energy efficiency and emissions are considered as objectives. • Dimensional reduction and decision-making method are coupled with NSGA-Ⅲ. • The cogeneration system is feasible and cost-effective for practical applications. [ABSTRACT FROM AUTHOR]

This paper considers the technical and economic feasibility of using renewable energy with hydrogen as the energy storage medium for two remote communities in Bhutan, selected to illustrate two common scenarios presenting different challenges. The Royal Government of Bhutan has published plans to provide electricity to all households in the next 20 years, but the practical problems of extending the grid over long distances and mountainous terrain will make that target difficult and expensive to achieve. Consequently, the possibility of using natural energy and diversified generation is attractive. This paper examines the use of hydro power in one community and photovoltaics with wind power in another. Hydrogen is the proposed energy storage medium in both cases. Analysis suggests that it is technically possible to use renewable energy and hydrogen for diversified power supplies and that where, as here, the costs of grid extension are high, it may also be financially viable. Thus we argue that there is a good case for establishing a test and demonstration system near the capital Thimphu for further investigation prior to use in remote locations. [Copyright &y& Elsevier]

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]

At present, renewable energy installations are expanding to solve both environmental problems and expensive energy fuel import prices in isolated areas. However, in a small-scale power system, rapid output fluctuations of renewable energy may cause power quality problems such as voltage and frequency fluctuations in the power system. To solve this problem, the local government of Jeju Island in South Korea implemented a megawatt (MW)-class pilot project to stabilize the output power of wind turbines using an energy storage system (ESS). In this project, a 0.5 MWh lithium-ion battery was connected to a 3 MW wind turbine via 1 MW power conversion system (PCS). In this paper, the field test results were divided into four categories as follows. First, the performance of stabilizing the output of the wind turbine using ESS was confirmed. Second, the control performance of the ESS was confirmed when the wind turbine suddenly stopped due to an accident. Third, it was confirmed that the ESS discharged energy into the power system after the stabilization of the wind turbine output. Fourth, the reasons for the failure of the ESS to control output stabilization of the wind turbine were analyzed through MATLAB simulation. [ABSTRACT FROM AUTHOR]

The storage of energy facilitates the management of renewable energy systems by reducing the mismatch between the supplied energy and the forecasted production due to forecasting errors. The storage increases the reliability of the renewable energy system and enables participation in the electricity market by committing to the sale of electricity for the following day. Nevertheless, the inclusion of the energy storage capacity requires the development of new management policies. In this paper, we propose a management strategy for a renewable energy system with storage capacity that integrates tactical and operational decisions in a single mathematical model that makes use of an updated probabilistic wind speed forecast. Management policies are obtained by solving a sequence of rolling-horizon stochastic optimization problems whose formulation is inspired by the Stochastic Approximation Average technique. The management policies are illustrated by their application to wind-farms using hydrogen as the energy storage medium. [ABSTRACT FROM AUTHOR]

The joint optimization of power systems, mobile energy storage systems (MESSs), and renewable energy involves complex constraints and numerous decision variables, and it is difficult to achieve optimization quickly through the use of commercial solvers, such as Gurobi and Cplex. To address this challenge, we present an effective joint optimization approach for MESSs and power grids that consider various renewable energy sources, including wind power (WP), photovoltaic (PV) power, and hydropower. The integration of MESSs could alleviate congestion, minimize renewable energy waste, fulfill unexpected energy demands, and lower the operational costs for power networks. To model the entire system, a mixed-integer programming (MIP) model was proposed that considered both the MESSs and the power grid, with the goal of minimizing costs. Furthermore, this research proposed a highly efficient deep reinforcement learning (DRL)-based method to optimize route selection and charging/discharging operations. The efficacy of the proposed method was demonstrated through many numerical simulations. [ABSTRACT FROM AUTHOR]

The United States has experienced heightened interest in clean energy sources, with many states implementing renewable technologies for energy generation. These efforts are critical for moving states and the nation toward decarbonization and decreased emissions within the transportation and power industries. A challenge with renewable sources (particularly solar and wind) is the inherent variability of energy generation due to climate and geographic influences. Consequently, there is increased interest in addressing energy storage technologies to better capitalize on excess power from high‐production periods for use in times of low generation. Energy storage comes in many different forms with varying duration. Several forms of energy storage are explored in this report to demonstrate the variety of technology options. Following research of the current state of energy storage policy, this work proposes three areas of potential policy improvements for industry: (1) implementation of a policy framework for states to produce ambitious energy storage procurement metrics; (2) amending of the federal investment tax credit for energy storage technologies to be duration‐based; and (3) incentivization of sustainable battery practices. It is imperative for state legislatures to create informed energy storage goals alongside current renewable energy metrics. Amending the modern federal investment tax credit system to include duration‐based incentivization will encourage the deployment of long‐duration technologies to combat variation in electricity generation over the course of seasons. Finally, for the continued deployment of batteries, sustainable practices must be implemented to mitigate material and environmental challenges posed by them, including recycling practices and alternative chemistry utilization. [ABSTRACT FROM AUTHOR]

• A novel stochastic-robust approach is developed for energy communities. • Peer-to-peer exchanges and collective assets are considered and analysed. • The problem is solved using a novel three-stage procedure. • Operating cost is incremented 77$ when the degree of robustness grows. • Collective storage systems help to reduce unserved demand and wasted generation. Energy communities enable effective coordination among prosumers on pursuing collective targets. This paper focuses on isolated 100% renewable communities, involving individual (controllable appliances and small generators) and collective (wind generators and battery banks) assets. To effectively coordinate the agents involved in these structures, advanced energy management strategies are necessary. This work develops a three-stage day-ahead scheduling strategy for isolated 100% energy communities, involving peer-to-peer transactions among prosumers. The different uncertainties involved are incorporated through a novel stochastic-robust formulation, that results in a computationally tractable optimization framework. To validate the new model, a case study on a six-prosumer benchmark community is analysed. Results reveal the importance of collective assets and peer-to-peer exchanges among prosumers as well as the effectiveness of the developed formulation. The role of batteries is also discussed, helping to reduce the total unserved energy and operating cost by 20% and 19%, respectively, as well as enabling a more efficient use of wind energy. The impact of robustness is also studied, incrementing the expected importable energy by 28% compared to the deterministic case, while the exportable energy from prosumers is notably reduced by 40%. However, uncertainty-aware strategies have a direct impact on operational costs, incrementing the expenditures by 37% when uncertainties are considered. [ABSTRACT FROM AUTHOR]

• Multi-objective modeling of a hydrogen-based sustainable energy system. • Scenario-based uncertainty modeling and scenario reduction using Kantorovich distance procedure • Risk-assessment using the downside risk constraints approach • Using the epsilon-constraint and fuzzy satisfying methods to find the optimal point Hydrogen is one of the leading energy sources under special consideration due to its sustainability and technological development. The main concept of integrated energy system is applied in this article. The goal is to maintain a coordination between hydrogen cycle where hydrogen is produced and converted with renewable energy (RE). Three energy storage for charging/discharging hydrogen, hot and cold water are employed enhance the energy efficiency. The demand-side energy system could rely on this combination that enables the increase in wind and solar energy utilization and uses it to generate hydrogen in future smart cities. This paper uses a scenario-based stochastic optimization to model the associated uncertainties. The epsilon-constraint and max-min fuzzy methods are used as multi-objective approach to satisfy environmental and economic goals, simultaneously. To analyze the associated risk imposed by multiple uncertain parameters, the downside risk constraint (DRC) is implemented to render several risk-averse strategies to the decision-maker based on various constraint level. The results show that the final solution in risk neutral strategy satisfies both emission and operation costs. The ultimate associated risk in conservative strategy will tend to zero at the 4.2% operation cost increment and 3% emission reduction. [ABSTRACT FROM AUTHOR]

With increasing shares of variable and uncertain renewable generation in many power systems, there is an associated increase in the importance of energy storage to help balance supply and demand. Gas networks currently store and transport energy, and they have the potential to play a vital role in longer-term renewable energy storage. Gas and electricity networks are becoming more integrated with quick-responding gas-fired power plants, providing a significant backup source for renewable electricity in many systems. This study investigates Ireland's gas network and operation when a variable green hydrogen input from excess wind power is blended with natural gas. How blended hydrogen impacts a gas network's operational variables is also assessed by modelling a quasi-transient gas flow. The modelling approach incorporates gas density and a compressibility factor, in addition to the gas network's main pressure and flow rate characteristics. With an increasing concentration of green hydrogen, up to 20%, in the gas network, the pipeline flow rate must be increased to compensate for reduced energy quality due to the lower energy density of the blended gas. Pressure drops across the gas pipeline have been investigated using different capacities of P2H from 18 MW to 124 MW. The results show significant potential for the gas network to store and transport renewable energy as hydrogen and improve renewable energy utilisation without upgrading the gas network infrastructure. [ABSTRACT FROM AUTHOR]

Hydrogen production via steam methane reforming (SMR) dominates the supply to refining complexes worldwide, resulting in significant greenhouse gas (GHG) emissions. There is a considerable demand for clean hydrogen pathways that are economically competitive with SMR. The development of a 563 MW integrated wind-hydrogen model with energy storage is proposed. The model utilizes real-time wind energy data to ascertain the optimal size of the electrolyser, the number of electrolyser units and the battery (energy storage) capacity that will yield a minimum hydrogen production cost, whilst functioning in a liberalized electricity market with dynamic prices. The optimal plant configuration consists of 81 units of a 3496 kW (760 Nm 3 /hr) electrolyser and 360 MWh (60 units) of battery capacity. For the minimum hydrogen production cost determined ($9.00//kg H 2 ), the wind farm accounts for 63% of this cost. Hence, if existing wind farm assets are used, such that the investment cost of building the wind-hydrogen plant does not include the wind farm costs, the hydrogen production cost is reduced to $3.37/kg H 2 . For a particular electrolyser-battery configuration, it was observed that the minimum hydrogen production cost occurs when their respective capacity factors are approximately equivalent. The benefits of energy storage are limited by the decrease in overall plant efficiency, which results from the use of the battery. For the techno-economic conditions considered in this paper, hydrogen production costs from wind powered electrolysis ($3.37 to $9.00/kg H 2 ) are uncompetitive with SMR/SMR-CCS ($1.87 to $2.60/kg H 2 ). [ABSTRACT FROM AUTHOR]