Simulation based size optimization of a PV/wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions

In this paper, size of a PV/wind integrated hybrid energy system with battery storage is optimized under various loads and unit cost of auxiliary energy sources. The optimization is completed by a simulation based optimization procedure, OptQuest, which integrates various heuristic methods. In the study, the main performance measure is the hybrid energy system cost. And the design parameters are PV size, wind turbine rotor swept area and the battery capacity. The case study is realized for Izmir Institute of Technology Campus Area, Urla, Turkey. The simulation model of the system is realized in ARENA 12.0, a commercial simulation software, and is optimized using the OptQuest tool in this software. Consequently, the optimum sizes of PV, wind turbine and battery capacity are obtained under various auxiliary energy unit costs and two different loads. The optimum results are confirmed using Loss of Load Probability (LLP) and autonomy analysis. And the investment costs are investigated how they are shared among those four energy sources at the optimum points.     

A novel renewable energy-based integrated system with thermoelectric generators for a net-zero energy house

A renewable energy based integrated system is developed to meet the total energy demands of a house located off-grid, and a thermodynamic analysis through energy and exergy methodologies is conducted for analysis, evaluation, and performance assessment. The present novel multigeneration system is mainly driven through the animal residues produced at the farm house. The proposed novel system is composed of nine main units namely, a biomass combustor, photovoltaic (PV) panels, parabolic solar trough collectors, thermoelectric generators, organic Rankine cycle, electrolyzer, homogeneous charged compression ignition (HCCI) engine, absorption chiller, and reverse osmosis (RO) unit. Biomass combustor runs an organic Rankine turbine for additional power during peak loads. The exhaust of gas turbine generates cooling to meet the cooling demand of the residential area of the farm house. PV panels are incorporated to generate hydrogen through electrolyzer. A HCCI engine generates power to compensate peak load as well as charging the farming vehicles of the farm house. The RO unit with energy recovery Pelton turbine produces fresh water for farming and residential use. The advanced integration of subsystems, thermoelectric generators and efficient utilization of waste, improves significant amount of energetic and exergetic efficiencies of overall multigenerational system. The energy and exergy efficiencies are enhanced in the order of 4.8% and 6.3%, respectively, after incorporating innovative cooling system to the PV modules. The overall energy and exergy efficiencies of the proposed multigeneration system with and without thermoelectric are found to be 67.6% and 57.1%, and 68.9% and 58.4%, respectively.     

Risk-constrained scheduling of a CHP-based microgrid including hydrogen energy storage using robust optimization approach

Recently, the integration of various energy resources, including renewable generation and combined heat and power (CHP) units in microgrids, has created the opportunity of off-grid operation with a suitable range of reliability. This paper presents an optimization model to schedule an islanded MG with various resources, including CHP, photovoltaic (PV), and boiler, as the primary energy provision sources besides electric battery storage, thermal storage and hydrogen energy system (HES). The HES has the power-to-hydrogen (P2H) and hydrogen-to-power (H2P) modes, which increases the flexibility of the scheduling. The uncertainty management is the most essential task in the CHP-based MGs scheduling problem, since the power and heat productions are interrelated and can result in economic losses without enough deliberations. Hence, this paper proposes the robust optimization approach (ROA) to cope with the uncertainties associated with the PV production and electric and heat load demands. The robust counterparts are applied to the deterministic problem to create a tractable adjustable robust framework. The problem is structured as a mixed-integer linear programming (MILP) handled by the General Algebraic Modeling System (GAMS) using CPLEX solver. The results verified the effectiveness of the proposed robust counterparts in managing the associated risk. The results illustrated a conscious scheduling strategy under robust conditions. However, the more preserved decisions are taken, the higher operational cost is realized. In this regard, the increment of robustness level from the lowest value (deterministic condition) to the highest value (conservatism condition) increased the operation cost by about 43.29%.     

Distributionally robust optimization of home energy management system based on receding horizon optimization

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.     

An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city

In this study, an inexact optimization modeling approach (IBEM: inexact Beijing energy model) was developed for supporting energy systems planning and air pollution mitigation under uncertainty. This model was based on the integration of multiple inexact optimization techniques, including interval-parameter programming, mixed-integer programming and chance-constrained programming, which make it have strength in dealing with uncertainties presented as both probabilistic distributions and interval numbers. The model could effectively facilitate systematic analysis of complexities associated with energy conversion and utilization, and air pollution mitigation. Particularly, it could help identify optimal patterns of energy resources allocation, as well as capacity expansion options for energy technologies under different air pollution emission reduction schemes. This could not only alleviate air pollution in the city, but also reduce the total system cost that was associated with various energy activities. Based on a two-step solution algorithm, useful solutions were generated, reflecting tradeoffs among environmental and economic conditions, and among different risk violation levels of constraints of the energy system in Beijing city. The interval solutions could then help decision makers identify desired policies for energy management and pollutions reduction.     

Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach

Recently, the increasing energy demand has caused dramatic consumption of fossil fuels and unavoidable raising energy prices. Moreover, environmental effect of fossil fuel led to the need of using renewable energy (RE) to meet the rising energy demand. Unpredictability and the high cost of the renewable energy technologies are the main challenges of renewable energy usage. In this context, the integration of renewable energy sources to meet the energy demand of a given area is a promising scenario to overcome the RE challenges. In this study, a novel approach is proposed for optimal design of hybrid renewable energy systems (HRES) including various generators and storage devices. The ε-constraint method has been applied to minimize simultaneously the total cost of the system, unmet load, and fuel emission. A particle swarm optimization (PSO)-simulation based approach has been used to tackle the multi-objective optimization problem. The proposed approach has been tested on a case study of an HRES system that includes wind turbine, photovoltaic (PV) panels, diesel generator, batteries, fuel cell (FC), electrolyzer and hydrogen tank. Finally, a sensitivity analysis study is performed to study the sensibility of different parameters to the developed model.     

Enhancing the utilization of photovoltaic power generation bysuperconductive magnetic energy storage

The authors demonstrate that a superconductive magnetic energy storage (SMES) system can enhance large-scale utilization of photovoltaic (PV) generation. Results show that power output from a SMES system can be used to smooth out PV power fluctuations so that the combined PV/SMES output is dispatchable and free from fluctuations. Power generated from PV arrays is shown to be fully utilized under different weather conditions, and PV penetration is increased to significant levels without adversely affecting the power system. Coupled with PV generation, a SMES system is even more effective in performing diurnal load leveling. A coordinated PV/SMES operation scheme is proposed, and its demonstration under different weather conditions is discussed     

Economic evaluation of reverse osmosis desalination system coupled with tidal energy

A reverse osmosis (RO) desalination system coupled with tidal energy is proposed. The mechanical energy produced by the tidal energy through hydraulic turbine is directly used to drive the RO unit. The system performances and the water cost of the conventional and tidal energy RO systems are compared. It is found that the proposed tidal energy RO system can save water cost in the range of 31.0%-41.7% in comparison with the conventional RO system. There is an optimum feed pressure that leads to the lowest water cost. The tidal RO system can save more costs at a high feed pressure or a high water recovery rate. The optimum feed pressure of the tidal energy RO system is higher than that of the conventional RO system. The longer lifetime of the tidal energy RO system can save even more water cost. When the site development cost rate is lower than 40%, the water cost of the tidal energy RO system will be lower than that of the conventional RO system. The proposed technology will be an effective alternative desalination method in the future.     

Identification of optimal strategies for energy management systems planning under multiple uncertainties

Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the planning of energy management systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority–inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management planning for a region with three cities. Useful solutions for the planning of energy management systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. Moreover, multiple uncertainties existing in the planning of energy management systems can be effectively addressed, improving robustness of the existing optimization methods.     

Multi-objective energy management approach in distribution grid integrated with energy storage units considering the demand response program

The penetration of renewable energy sources and energy storage (ES) units into the distribution system has increased, and it is important to examine their effect on the systems' operation scheme and security. Voltage stability index is defined as a security objective function, and its improvement by energy management in the distribution network is a major challenge in this study. The dynamic distribution feeder reconfiguration (DDFR) is introduced as an efficient approach for energy management in the distribution network, considering energy loss, voltage stability index, and operational cost as objective functions in the presence of distributed generators, solar PV panels, ES units, and capacitors. The demand response program, including interruptible/curtailable service, is proposed to enable energy consumers to rethink their energy consumption patterns based on incentive and punitive policies. A modified particle swarm optimization algorithm is presented to solve the considered optimization problem. The suggested approach is tested on the 95-node test system and its superiorities are shown through comparison with other evolutionary algorithms. Based on the obtained results, after presenting a new model based on DDFR for energy management in the distribution system, energy loss and voltage stability are reduced by 25% and 2.8%, respectively. After applying the demand response program in the proposed model, energy loss and operational cost are reduced by 26% and 5.9%, respectively.     

Quantifying self-consumption of on-site photovoltaic power generation in households with electric vehicle home charging

Photovoltaic (PV) power production and residential power demand are negatively correlated at high latitudes on both annual and diurnal basis. If PV penetration levels increase, methods to deal with power overproduction in the local distribution grids are needed to avoid costly grid reinforcements. Increased local consumption is one such option. The introduction of a home-charged plug-in electric vehicle (PEV) has a significant impact on the household load and potentially changes the coincidence between household load and photovoltaic power production. This paper uses a stochastic model to investigate the effect on the coincidence between household load and photovoltaic power production when including a PEV load. The investigation is based on two system levels: (I) individual household level and (II) aggregate household level. The stochastic model produces theoretical high-resolution load profiles for household load and home charged PEV load over time. The photovoltaic power production model is based on high-resolution irradiance data for Uppsala, Sweden. It is shown that the introduction of a PEV improves the self-consumption of the photovoltaic power both on an individual and an aggregate level, but the increase is limited due to the low coincidence between the photovoltaic power production pattern and the charging patterns of the PEV.     

Dynamic modeling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology

Economic and environmental concerns over fossil fuels encourage the development of photovoltaic (PV) energy systems. Due to the intermittent nature of solar energy, energy storage is needed in a stand-alone PV system for the purpose of ensuring continuous power flow. Three stand-alone photovoltaic power systems using different energy storage technologies are studied in this paper. Key components including PV modules, fuel cells, electrolyzers, compressors, hydrogen tanks and batteries are modeled in a clear way so as to facilitate the evaluation of the power systems. Based on energy storage technology, a method of ascertaining minimal system configuration is designed to perform the sizing optimization and reveal the correlations between the system cost and the system efficiency. The three hybrid power systems, i.e., photovoltaic/battery (PV/Battery) system, photovoltaic/fuel cell (PV/FC) system, and photovoltaic/fuel cell/battery (PV/FC/Battery) system, are optimized, analyzed and compared. The obtained results indicate that maximizing the system efficiency while minimizing system cost is a multi-objective optimization problem. As a trade-off solution to the problem, the proposed PV/FC/Battery hybrid system is found to be the configuration with lower cost, higher efficiency and less PV modules as compared with either single storage system.     

Development of an interval multi‐stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty

A regional energy system consists of diverse forms of energy. Energy‐related issues such as utilization of renewable energy and reduction of greenhouse gas (GHG) emission are confronting decision makers. Meanwhile, various uncertainties and dynamics of the energy system are posing difficulties for the energy system planning, especially for those under multiple stages. In this study, an interval multi‐stage stochastic programming regional energy systems planning model (IMSP‐REM) was developed to support regional energy systems management and GHG control under uncertainty. The IMSP‐REM is a hybrid methodology of inexact optimization and multi‐stage stochastic programming. Not only can it handle uncertainties presented as intervals and probability density functions but also reflect dynamics of system conditions over multiple planning stages. The developed IMSP‐REM was applied to a hypothetical regional energy system. The results indicate that the IMSP‐REM can effectively reflect issues of GHG reduction and renewable energy utilization within an energy system planning framework. In addition, the model has advantages in incorporating multiple uncertainties and dynamics within energy management systems. Copyright © 2011 John Wiley & Sons, Ltd.     

A model for optimal sizing of photovoltaic irrigation water pumping systems

The previous methods for optimal sizing of photovoltaic (PV) irrigation water pumping systems separately considered the demand for hydraulic energy and possibilities of its production from available solar energy with the PV pumping system. Unlike such methods, this work approaches the subject problem systematically, meaning that all relevant system elements and their characteristics have been analyzed: PV water pumping system, local climate, boreholes, soil, crops and method of irrigation; therefore, the objective function has been defined in an entirely new manner. The result of such approach is the new mathematical hybrid simulation optimization model for optimal sizing of PV irrigation water pumping systems, that uses dynamic programming for optimizing, while the constraints were defined by the simulation model. The model was tested on two areas in Croatia, and it has been established that this model successfully takes into consideration all characteristic values and their relations in the integrated system. The optimal nominal electric power of PV generator, obtained in the manner presented, are relatively smaller than when the usual method of sizing is used. The presented method for solving the problem has paved the way towards the general model for optimal sizing of all stand-alone PV systems that have some type of energy storage, as well as optimal sizing of PV power plant that functions together with the storage hydroelectric power plant.     

Modeling and performance analysis of renewable hydrogen energy hub connected to an ac/dc hybrid microgrid

This paper proposes a system modeling and performance analysis of a renewable hydrogen energy hub (RHEH) connected to an ac/dc hybrid microgrid (MG). The proposed RHEH comprises a photovoltaic (PV)-based renewable energy source (RES) as the primary source, a proton exchange membrane fuel cell (PEMFC) as the secondary power source, and a proton exchange membrane electrolyzer (PEMELZ) that can generate and store hydrogen in a hydrogen tank. All these resources are directly connected at the dc bus of the ac/dc microgrids. The PEMFC operates and utilizes the hydrogen from the hydrogen tank when the energy generated by RES cannot meet the load demand. A coordinated power flow control approach has been developed for the RHEH to mitigate the mismatch between generation and demand in the ac/dc microgrid and produce renewable hydrogen when renewable power is in excess. The paper also proposes a modified hybrid Perturb & Observe-Particle Swarm Optimization (Hybrid PO-PSO) algorithm to ensure the maximum power point tracking (MPPT) operation of the PV and the PEMFC. The operation of the proposed RHEH is validated through simulations under various critical conditions. The results show that the proposed RHEH is effective to maintain the system power balance and can provide power-to-hydrogen and hydrogen-to-power when required.     

Performance of reverse osmosis pilot plant powered by Photovoltaic in Jordan

This paper presents the work performed within ADIRA project funded by European Union as a consortium of many countries to boost the integration of desalination units with renewable energy in rural areas. The work focused on the study of Water desalination by Reverse Osmosis (RO) and electricity generation using Photovoltaic Technology (PV) with additional battery storage. RO-PV system has been successfully designed, installed and tested in Hartha Charitable Society in northern part of Jordan as part of the ADIRA project. The system is composed of PV panels (433 Wp), softener and compact RO unit with typical daily water production of 500 L. The system produced clean drinking water from a salty water feed with salt content up to 1700 mg/L. The technical details of the RO plant, the energy supply and the operation strategies of the system are presented in this study. The effect of meteorological data like solar insolation, ambient temperature, daily sunshine hours on the performance of the RO-PV system are studied. Furthermore, effect of operating pressure and temperature on recovery percentage, salt rejection and specific energy consumption in addition to details about the PV current and voltage are also discussed.     

Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects

Along with the continuing global warming, the environmental constraints are expected to play more and more important role in the operation of distributed energy resource (DER) systems, besides the economic objective. In this study, a multi-objective optimization model is developed to analyze the optimal operating strategy of a DER system while combining the minimization of energy cost with the minimization of environmental impact which is assessed in terms of CO₂ emissions. The trade-off curve is obtained by using the compromise programming method. As an illustrative example, the DER system installed in an eco-campus in Japan has been selected for case study. The distributed technologies under consideration include photovoltaics (PV), fuel cell and gas engine for providing electrical and thermal demands. The obtained results demonstrate that increasing the satisfaction degree of economic objective leads to increased CO₂ emissions. The operation of the DER system is more sensitive when environmental objective is paid more attention. Moreover, according to the sensitivity analysis, the consideration of electricity buy-back, carbon tax, as well as fuel switching to biogas, has more or less effect on the operation of DER systems.     

Distributed optimization of electricity-Gas-Heat integrated energy system with multi-agent deep reinforcement learning

The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES) has the characteristics of strong coupling, non-convexity, and nonlinearity. The centralized optimization method has a high cost of communication and complex modeling. Meanwhile, the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency, which is difficult to apply online. For the coordinated optimization problem of the electricity-gasheat IES in this study, we c...     

An inexact robust optimization method for supporting carbon dioxide emissions management in regional electric-power systems

In this study, an inexact robust optimization method (IROM) is developed for supporting carbon dioxide (CO₂) emission management in a regional-scale energy system, through incorporating interval-parameter programming (IPP) within a robust optimization (RO) framework. In the modeling formulation, penalties are exercised with the recourse against any infeasibility, and robustness measures are introduced to examine the variability of the second-stage costs that are above the expected levels. The IROM is suitable for risk-aversive planners under high-variability conditions. The IROM is applied to a case of energy systems and CO₂ emission planning under uncertainty. The results obtained can generate desired decision alternatives that are able to not only enhance electricity-supply safety with a low system-failure risk level but also mitigate CO₂ emissions. They can be used for generating decision alternatives and minimizing the system cost of energy system while meeting the CO₂-emission permit requirement.     

Modelling and optimization of the smart hybrid renewable energy for communities (SHREC)

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.