Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

Thermoeconomic installation limit of PV/WT hybrid energy systems for Off-grid islands

An ideal off-grid island can become 100% energy-sufficient if one installs renewable energy systems such as solar photovoltaic (PV) and wind turbine (WT) systems. However, the intermittent and uncertain nature of the power supply from renewable energy systems hinders a 100% autonomy level (AL) without an infinite energy storage capacity. The thermoeconomic installation limit (TEIL) of a PV/WT hybrid energy system was studied using hourly weather data and the energy demand profile for off-grid islands. An appropriate battery size for the TEIL was also determined. Given the current installation cost of the hybrid energy system and the battery unit, the AL for a PV/WT hybrid energy system at the TEIL is calculated to be approximately 70%. Above the limit, the size of the energy storage unit and, correspondingly, the total annual cost of the PV/WT hybrid energy system increase sharply.     

Optimal sizing and siting of renewable energy resources in distribution systems considering time varying electrical/heating/cooling loads using PSO algorithm

Distributed Generation (DG) sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to their environmental friendliness and also the limited sources of fossil fuels. In general, the optimal location and size of DG units have profoundly impacted on the system losses in a distribution network. In the present article, the Particle Swarm Optimization (PSO) algorithm is employed to find the optimal location and size of DG units in a distribution system. The optimal location and size of DG units are determined on the basis of a multi-objective strategy as follows: (i) the minimization of network power losses, (ii) the minimization of the total costs of Distributed Energy Resources (DERs), (iii) the improvement of voltage stability, and (iv) the minimization of greenhouse gas emissions. The related distribution system was assumed to be composed of the fuel cells, wind turbines, photovoltaic arrays, and battery storages. The electrical, cooling, and heating loads were also considered in this article. The heating and cooling requirements of the system consist of time varying water heating load, space heating load, and space cooling load. In this study, the waste and fuel cell were used to produce the required heating and cooling loads in the distribution system. In addition, the absorption chiller was used to supply the required space cooling loads. A detailed performance analysis was carried out on 13 bus radial distribution system to demonstrate the effectiveness of the proposed methodology.     

Optimal Design Methods for Hybrid Renewable Energy Systems

Renewable and hybrid energy systems (HESs) are expanding due to environmental concerns of climate change, air pollution, and depleting fossil fuels. Moreover, HESs can be cost effective in comparison with conventional power plants. This article reviews current methods for designing optimal HESs. The survey shows these systems are often developed on a medium scale in remote areas and stand-alone, but there is a global growing interest for larger scale deployments that are grid connected. Examples of HESs are PV–wind–battery and PV–diesel–battery. PV and wind energy sources are the most widely adopted. Diesel and batteries are often used but hydrogen is increasing as a clean energy carrier. The design of an efficient HES is challenging because HES models are nonlinear, non-convex, and composed of mixed-type variables that cannot be solved by traditional optimization methods. Alternatively, two types of approaches are typically used for designing optimal HESs: simulation-based optimization and metaheuristic optimization methods. Simulation-based optimization methods are limited in view of human intervention that makes them tedious, time consuming, and error prone. Metaheuristics are more efficient because they can handle automatically a range of complexities. In particular, multi-objective optimization (MOO) metaheuristics are the most appropriate for optimal HES because HES models involve multiple objectives at the same time such as cost, performance, supply/demand management, grid limitations, and so forth. This article shows that the energy research community has not fully utilized state-of-the-art MOO metaheuristics. More recent MOO metaheuristics could be used such as robust optimization and interactive optimization.     

Triple-objective optimal sizing based on dynamic strategy for an islanded hybrid energy microgrid

A triple-objective optimal sizing method based on a dynamic strategy is presented for an islanded hybrid energy microgrid, consisting of wind turbine, solar photovoltaic, battery energy storage system and diesel generator. The dynamic strategy is given based on a dynamic complementary coordination between two different master-slave control modes for maximum renewable energy utilization. Combined with the proposed strategy, NSGA-II-based optimization program is applied to the sizing optimization problem with triple different objectives including the minimization of annualized system cost, the minimization of loss of power supply probability and the maximization of utilization ratio of renewable energy generation. The sizing results and the proposed strategy are both compared and analyzed to validate the proposed method in a real case of an islanded hybrid energy microgrid on Dong’ao Island, China.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

Optimization of grid tied hybrid power system in smart premises

Tapping of renewable energy sources like solar and wind is given great priority by power producers all over the world. Technical problems of linking them to the grid are solved. The cost constraints of utilizing renewable energy at specific locations are to be determined. In this work, a model is developed for grid tied hybrid power system (HPS) consisting of photovoltaic (PV) module and wind mill at the roof top of smart premises. The grid is capable of delivering and receiving energy. Objective function is formed with constraints taking into account the cost of PV module, wind mill, and grid tied inverter with controller. The constraints are rating of HPS and energy that can be delivered to the grid. Using this model, case studies were conducted in three locations in India, each location having two different demands. The results are presented. With the optimal rating of HPS, results shows that, conventional energy cost is higher.     

A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms

Meta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.     

Thermal Modeling and Performance Evaluation of Photovoltaic Thermal (PV/T) Systems: A Parametric Study

Conventional solar photovoltaic (PV) module converts the light component of solar radiation into electrical power, and heat part is absorbed by module increasing its operating temperature. Combined PV module and heat exchanger generating both electrical and thermal powers is called as hybrid photovoltaic/thermal (PV/T) solar system. The paper presents the design of a PV/T collector, made with thin film PV technology and a spiral flow absorber, and a simulation model, developed through the system of several mathematical equations, to evaluate the performance of PV/T water collectors. The effect of various parameters on the thermal and electrical efficiency has been investigated to obtain optimum combination of parameters. Finally, a numerical simulation has been carried out for the daily and annual yield of the proposed PV/T collector, and comparison with a standard PV module is discussed.     

Sizing and performance analysis of standalone hybrid photovoltaic/battery/hydrogen storage technology power generation systems based on the energy hub concept

In this study, the optimal sizing and performance analysis of a standalone integrated solar power system equipped with different storage scenarios to supply the power demand of a household is presented. One of the main purposes when applying solar energy resource is to face the increasing environmental pollutions resulting from fossil fuel based electricity sector. To this end, and to compare and examine two energy storage technologies (battery and hydrogen storage technology), three storage scenarios including battery only, hydrogen storage technology only and hybrid storage options are evaluated. An optimization framework based on Energy Hub concept is used to determine the optimum sizes of equipment for the lowest net present cost (NPC) while maintaining the system reliability. It was determined that the most cost effective and reliable case is the system with hybrid storage technology. Also, the effects of solar radiation intensity, the abatement potential of CO₂ emissions and converting excess power to hydrogen on the system’s performance and economics, were investigated and a few noticeable findings were obtained.     

A novel framework-based cuckoo search algorithm for sizing and optimization of grid-independent hybrid renewable energy systems

Hybrid renewable energy systems (HRES) turned into an appealing choice for supplying loads in remote areas. The application of smart grid principals in HRES provides a communication between the load and generation from the HRES. Using smart grid in the HRES will optimally utilize the generating resources to reschedule the loads depending on its importance. This paper presents a new proposed design and optimization simulation program for techno-economic sizing of grid-independent hybrid PV/wind/diesel/battery energy system using Cuckoo search (CS) optimization algorithm. Using of CS will help to get the global minimum cost condition and prevent the simulation to be stuck around local minimum. A new proposed simulation program (NPSP) is acquainted using CS to determine the optimum size of each component of the HRES for the lowest cost of generated energy and the lowest value of dummy energy, at highest reliability. A detailed economic methodology to obtain the price of the generated energy has been introduced. Results showed that using CS reduced the time required to obtain the optimal size with higher accuracy than other techniques used iterative techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Numerous significant outcomes can be extracted from the proposed program that could help scientists and decision makers.     

中国煤电企业环境压力测试

中国煤电行业的发展伴随着各种环境风险,本文以典型煤电企业为例,在产能过剩、能效标准提高、环境保护税、全国碳市场、水资源税和非水可再生能源规划目标的风险约束下,建立环境成本内部化和环境风险分析工具,考虑不同压力情境下对煤电企业价值的影响,构建环境风险影响财务成本的压力测试框架。结果表明,对单个风险而言,产能过剩和碳市场风险对企业价值的影响是大多数地区在不同情景中面临的主要风险驱动因素。对于综合风险压力测试而言,各地区1 000MW超超临界机组乐观情景及悲观情景的企业价值距合理回报预期企业价值相差小,而300MW和600MW亚临界机组因能效水平低、经营成本高等原因在环境风险压力下企业价值偏离合理回报较多。随着环境风险严重程度的不断增加,环境压力测试有助于煤电企业和金融机构了解环境风险对企业财务状况的影响,从而对投资决策产生影响。     

Determination of the effect of operating cost uncertainty on mining project evaluation

Mining projects are complex businesses that demand constant risk assessment. This is because several kinds of uncertainties influence the value of a mine project, typically. These uncertainties may be classified as exploration uncertainties, economic uncertainties and engineering uncertainties. The evaluation of a mine project under these uncertainties is a complicated job, which may lead to making a wrong decision by managers and stockholders. Therefore, at first, the engineers must recognize the mining uncertainties before carrying out the project evaluation. The economic uncertainties are the most important factors, which may affect the project evaluation. Among the mentioned uncertainties, the operating cost uncertainty is an important and effective factor, which is ignored to a certain extent.     

Two-stage planning for sustainable water-quality management under uncertainty

In water-quality management problems, uncertainties may exist in a number of impact factors and pollution-related processes (e.g., the volume and strength of industrial wastewater and their variations can be presented as random events through identifying a statistical distribution for each source); moreover, nonlinear relationships may exist among many system components (e.g., cost parameters may be functions of wastewater-discharge levels). In this study, an inexact two-stage stochastic quadratic programming (ITQP) method is developed for water-quality management under uncertainty. It is a hybrid of inexact quadratic programming (IQP) and two-stage stochastic programming (TSP) methods. The developed ITQP can handle not only uncertainties expressed as probability distributions and interval values but also nonlinearities in the objective function. It can be used for analyzing various scenarios that are associated with different levels of economic penalties or opportunity losses caused by improper policies. The ITQP is applied to a case of water-quality management to deal with uncertainties presented in terms of probabilities and intervals and to reflect dynamic interactions between pollutant loading and water quality. Interactive and derivative algorithms are employed for solving the ITQP model. The solutions are presented as combinations of deterministic, interval and distributional information, and can thus facilitate communications for different forms of uncertainties. They are helpful for managers in not only making decisions regarding wastewater discharge but also gaining insight into the tradeoff between the system benefit and the environmental requirement.     

Can Desalination and Clean Energy Combined Help to Alleviate Global Water Scarcity?

The major present hindrance in using desalination to help alleviate global water scarcity is the cost of this technology, which, in turn is due to energy cost involved. This study examines historical trends in desalination and breaks up the cost of desalination into energy based and nonenergy based. It then develops the learning curves (relationship between cumulative production and market price) for desalination. Assuming that the photovoltaic (PV) technology will be the dominant form of energy used in the desalination process, the existing PV learning curve and desalination learning curve are combined to explore the viability of large‐scale adoption of desalination in the future. The world has been divided into seven regions and it is assumed that water demand from desalinated water will be met only within the 100‐km coastal belt. It is shown that, in most of the regions, other than sub‐Saharan Africa, Central America, and South Asia (where water tariffs are low), the desalination (without considering energy) becomes viable by 2040. For PV technology, less than 1 million MW per annum growth is required till 2050 to make it affordable. Globally, desalination with renewable energy can become a viable option to replace domestic and industrial water demand in the 100‐km coastal belt by 2050.     

Inexact credibility constrained programming for environmental system management

This study proposed an inexact credibility constrained programming (ICCP) to deal with multi-formats of uncertainties in parameters and variables for an agricultural water planning system. The study system includes three subareas with different crop distributions. The redundant water in the wet season can be stored in the reservoir and utilized in the dry season. The ICCP method can reflect not only inexact uncertainties in the objective function, variables and parameters, but also fuzzy uncertainties in the right-hand side. Interval credibility levels which represent satisfaction degrees of the constraints can be analyzed. Scenario analysis is conducted to analyze possible events in wet and dry years. The resulting solutions can provide stable intervals for the objective function and decision variables with different levels of risk when violating the constraints.     

Comparative assessment of solar photovoltaic panels based on metal-derived hazardous waste,resource depletion,and toxicity potentials

Solar photovoltaic (PV) cells are used to resolve energy security and climate change problems. Although PV panels have long physical lifetimes, they would be eventually replaced by new ones with higher energy efficiency and then changed to waste. Depending on the types of PV cells, waste PV panels have different environmental impact potentials due to different contents of substances. This study assesses and compares hazardous waste, resource depletion, and toxicity potentials from metals in three types of PV modules (i.e., polycrystalline silicon (Si), amorphous Si, and CIGS (copper/indium/gallium/di-selenite) PVs) on per-watt electricity generation basis. Hazardous waste potentials are examined by using metal leachability tests, and resource depletion and toxicity potentials are evaluated by using life cycle impact assessment methods. The polycrystalline Si and CIGS PVs have hazardous waste potentials due to lead (Pb) and cadmium/selenium, respectively, whereas the amorphous Si PV does not. The polycrystalline Si PV has the highest resource depletion potential due primarily to silver; the CIGS PV has the next highest due primarily to selenium; and the amorphous Si PV had the lowest, which is derived primarily from tin and copper. For toxicity potentials, overall the amorphous Si PV had lower potentials, derived primarily from barium/copper/nickel/zinc, than the polycrystalline Si and CIGS PVs of which the toxicity potentials were primarily form copper/lead/nickel/silver and copper/mercury/molybdenum/nickel/silver, respectively. Therefore, waste polycrystalline Si and CIGS PV panels should be recycled and managed with priority, and PV technology development needs to be directed to amorphous Si PV from the material perspective.     

A wind-solar PV hybrid power system with battery backup for water pumping in remote localities

After energy, water is the most critical commodity to be made available to people to keep them alive. Saudi Arabia has vast land and people are living in all regions. Most of these are connected to national grid but some are not, especially in remote areas like in the north, south, and west south. Pumping water in remote areas for domestic needs like agriculture and animals beside human needs is essential and require regular power supply. The present idea of wind-PV-Battery hybrid power system based on 100% renewable source is being proposed to utilize and tested in some of the regions on experimental bases. Of the five locations chosen for the purpose, namely Dhahran, Riyadh, Jeddah, Guriat and Nejran, some are good from both wind and solar intensity point of view some have good winds only and some good solar only. Nearly optimal size of PV-Wind water pumping system is determined for each of these sites considering the availability of solar and wind energy distributions throughout the year in these sites. It is shown that the monthly total water pumping capacity when using nearly optimal PV-Wind water pumping system is fairly uniform throughout the year except for the sites of Guriat and Riyadh. In these sites higher water pumping capacity is observed during the spring and summer months. On the other hand the cost of underground water pumping is found to vary between 6 to 12 US¢/m³ for the five sites considered.     

An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection

Cut-off grade strategy (COGS) is a concept that directly influences the financial, technical, economical, and environmental issues in relation to the exploitation of a mineral resource. Despite the simple definition of cut-off grade, the COGS problem is one of the complex and complicated problems in the mine planning process. From the optimization point of view, the COGS with an objective of maximizing the present value of future cash flows is a non-linear and a non-convex problem that even in its deterministic form can be solved using approximate optimization methods. This optimization problem will also be more complex and complicated under uncertainty conditions. This paper proposes an uncertainty based multi-criteria ranking system to investigate the problem of COGS selection considering metal price and geological uncertainties. The proposed system aims at selection of the best COGS among technically feasible alternative COGSs under uncertainty circumstances. Our developed system is based on integrating metal price and geological uncertainties as well as operating flexibility to close the mine early. We incorporate this operating flexibility into the proposed system using a Monte Carlo based real options (RO) valuation model. For this purpose, in addition to the expected value, other risk criteria are considered to rank the alternatives. These risk criteria include abilities of strategies in producing extra profits, minimizing losses, and achieving the predefined goals of the production. In this study, the technically possible COGSs are generated using the Lane comprehensive algorithm. To demonstrate the effectiveness of the proposed system, we utilize data of an Iranian gold mine. Results show that the proposed system outperforms conventional methods in the sense that it shows significantly lower average mis-ranking than the other methods and also selects a strategy with a higher value. The sensitivity analysis of the proposed system relative to the gold price shows that the system is highly dependent on the parameters of the stochastic process used to model the evolution of the metal price. Therefore, special consideration should be given in estimating stochastic process parameters.