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.     

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.     

GROUND WATER MANAGEMENT OPTIMIZATION USING GENETIC ALGORITHMS AND SIMULATED ANNEALING: FORMULATION AND COMPARISON1

ABSTRACT: Genetic algorithms (GA) and simulated annealing (SA), two global search techniques, are coupled with MODFLOW, a commonly used groundwater flow simulation code, for optimal management of ground water resources under general conditions. The coupled simulation-optimization models allow for multiple management periods in which optimal pumping rates vary with time to reflect the changing flow conditions. The objective functions of the management models are of a very general nature, incorporating multiple cost terms such as the drilling cost, the installation cost, and the pumping cost. The models are first applied to two-dimensional maximum yield and minimum cost water supply problems with a single management period, and then to a multiple management period problem. The strengths and limitations of the GA and SA based models are evaluated by comparing the results with those obtained using linear programming, nonlinear programming, and differential dynamic programming. For the three example problems examined in this study, the GA and SA based models yield nearly identical or better solutions than the various programming methods. While SA tends to outperform GA in terms of the number of forward simulations needed, it uses more empirical control parameters which have significant impact on solution efficiency but are difficult to determine.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

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.     

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.     

Automated energy management in distributed electricity systems: An EEPOS approach

The increasing capacity of distributed electricity generation brings new challenges in maintaining a high security and quality of electricity supply. New techniques are required for grid support and power balance. The highest potential for these techniques is to be found on the part of the electricity distribution grid.

This article addresses this potential and presents the EEPOS project’s approach to the automated management of flexible electrical loads in neighborhoods. The management goals are (i) maximum utilization of distributed generation in the local grid, (ii) peak load shaving/congestion management, and (iii) reduction of electricity distribution losses. Contribution to the power balance is considered by applying two-tariff pricing for electricity.

The presented approach to energy management is tested in a hypothetical sensitivity analysis of a distribution feeder with 10 households and 10 photovoltaic (PV) plants with an average daily consumption of electricity of 4.54 kWh per household and a peak PV panel output of 0.38 kW per plant. Energy management shows efficient performance at relatively low capacities of flexible load. At a flexible load capacity of 2.5% (of the average daily electricity consumption), PV generation surplus is compensated by 34–100% depending on solar irradiance. Peak load is reduced by 30% on average. The article also presents the load shifting effect on electricity distribution losses and electricity costs for the grid user.     

Optimization of the Resources Management in Fighting Wildfires

Wildfires lead to important economic, social, and environmental losses, especially in areas of Mediterranean climate where they are of a high intensity and frequency. Over the past 30 years there has been a dramatic surge in the development and use of fire spread models. However, given the chaotic nature of environmental systems, it is very difficult to develop real-time fire-extinguishing models. This article proposes a method of optimizing the performance of wildfire fighting resources such that losses are kept to a minimum. The optimization procedure includes discrete simulation algorithms and Bayesian optimization methods for discrete and continuous problems (simulated annealing and Bayesian global optimization). Fast calculus algorithms are applied to provide optimization outcomes in short periods of time such that the predictions of the model and the real behavior of the fire, combat resources, and meteorological conditions are similar. In addition, adaptive algorithms take into account the chaotic behavior of wildfire so that the system can be updated with data corresponding to the real situation to obtain a new optimum solution. The application of this method to the Northwest Forest of Madrid (Spain) is also described. This application allowed us to check that it is a helpful tool in the decision-making process.     

温度冲击试验设备实现途径及热负荷分析

结合试验指标要求,对温度冲击试验设备实现途径进行分析比较,选择两箱式作为试验设备的实现方式;对设备组成结构及制冷、加热流程进行阐述,并对温度冲击试验过程中两种制冷状态下的热负荷进行了分析计算。     

Effect of different design aspects of pipe for earth air tunnel heat exchanger system: A state of art

Earth air tunnel heat exchanger (EATHE) is a promising passive technique to provide thermal comfort condition in buildings. EATHE system uses undisturbed temperature of the ground for heating/cooling of air. Despite the several advantages, this technique has not become much popular owing to its high capital cost (mainly pipe cost and trench excavation cost) and large land area requirements. The primary objective of this study is to present a comprehensive review of different EATHE pipe layouts, pipe properties and positioning of the pipe with their advantages and limitations. It is observed that the ring pipe layout is the most cost-effective pipe-layout for small size EATHE system because it saves excavation cost by using a trench of the existing foundation of the building. However, Grid pipe-layout is an ideal layout for a large size EATHE system. Multi-layer pipe layouts should be used to reduce the land area requirement significantly. Moreover, EATHE system can be installed beneath the building (under building foot-print) to eliminate extra land area requirement for the installation of EATHE system. This review article shows that the Initial capital cost and land area requirement for the EATHE system can be substantially reduced by using appropriate pipe layout. It can be concluded that if EATHE system is installed with proper design strategies, it will be a clean and cost-effective method for building heating/cooling with significant power savings.     

A river water quality management model for optimising regional wastewater treatment using a genetic algorithm

To achieve water quality goals and wastewater treatment cost optimisation in a river basin, a water quality management model has been developed through the integration of a genetic algorithm (GA) and a mathematical water quality model. The developed model has been applied to the Youngsan River, where water quality has decreased due to heavy pollutant loads from Kwangju City and surrounding areas. Pollution source, land use, geographic features and measured water quality data of the river basin were incorporated into the Arc/View geographic information system database. With the database, the management model calculated treatment type and treatment cost for each wastewater treatment plant in the river basin. Until now, wastewater treatment policy for polluted rivers in Korea has been, first of all, to construct secondary treatment plants for untreated areas, and secondarily, to construct advanced treatment plants for the river sections whose water quality is impaired and for which the water quality goal of the Ministry of Environment is not met. Four scenarios that do not use the GA were proposed and they were compared with the results of the management model using the GA. It became clear that the results based on the GA were much better than those for the other four scenarios from the viewpoint of the achievement of water quality goals and cost optimisation.     

A CLOSED WATER REUSE SYSTEM FOR POWER PLANT COOLING AND DIGESTER HEATING1

ABSTRACT: A model consisting of closed water reuse and productive use of various types of wastes for energy generation is presented. The sewage after treatment would be used as the cooling water for power plants, and the condenser discharge therefrom be used as heating water for sludge digesters. The water is then purified for municipal water supply for continuous use. The advantages of this system are that water resources and energy are conserved while various types of wastes including waste heat are controlled. With a preliminary system analysis, it appears that the design for power plant based on the total heating value of wastes and digester capacity based on sewage sludge generation is feasible in terms of acquisition and full utilization of various types of wastes as generated in a single metropolitan area. The system as shown in this design is in balance among various factors such as the generation rate of municipal refuse, municipal sewage, waste heat in the condenser discharge, and raw sewage sludge.     

Optimum Heat Conductance Distribution for Power Optimization of a Regenerated Closed Brayton Cycle

In this paper, the power output of the cycle is taken as objective for performance optimization of an irreversible regenerated closed Brayton cycle coupled to constant-temperature thermal energy reservoirs in the viewpoint of finite time thermodynamics (FTT) or entropy generation minimization (EGM). The analytical formulae about the relations between power output and pressure ratio are derived with the heat resistance losses in the hot- and cold-side heat exchangers and the regenerator, the irreversible compression and expansion losses in the compressor and turbine, and the pressure drop loss in the piping. The maximum power output optimization is performed by searching the optimum heat conductance distribution corresponding to the optimum power output among the hot- and cold-side heat exchangers and the regenerator for the fixed total heat exchanger inventory. The influence of some design parameters, including the temperature ratio of the heat reservoirs, the total heat exchanger inventory, the efficiencies of the compressor and the turbine, and the pressure recovery coefficient, on the optimum heat conductance distribution and the maximum power output are provided. The power plant design with optimization leads to smaller size including the compressor, turbine, and the hot- and cold-side heat exchangers and the regenerator.     

STATUS REPORT ON HEAT STORAGE WELLS1

ABSTRACT. The Heat Storage Well concept evolved at TEMPO in 1972. Technical, economic, institutional, and legal aspects of implementing the concept are being studied. Heat Storage Wells offer the possibility of conserving a substantial part of the heat energy now wasted in generating electricity and of reducing thermal pollution caused by the waste heat. Large thermal electric power plants would produce heat at useful temperatures such as 300–400°F. Combined electric- and heat-generating systems will require low-cost, low-loss storage of large amounts of hot water for periods of 90 to 180 days to serve both electric loads and seasonally-varying heat loads. Compared to conventional electricity-only systems, combined electricity-heat systems can save more than 20 percent in energy, reduce the cost of both electricity and heat, and eliminate the need for cooling water or towers. Possibilities for changes in legal and institutional practices are suggested, such as making resource-allocation decisions on the basis of energy units rather than dollars.     

Evaluation of desiccant wheel and prime mover as combined cooling,heating, and power system

In this research heating, cooling and electrical demands of a residential tower are evaluated for Iran various weather conditions. For this purpose, several cities are selected as the representative of the specific weather conditions. To meet the cooling demand, desiccant cooling system plus alternative systems are applied. To analyze desiccant wheel, outlet humidity and temperature have been modeled. Also, the effect of rotational speed and regeneration temperature on entropy generation of the desiccant wheel has been studied based on the obtained results. It is deduced that the entropy generation may be increased by increasing the regeneration temperature and the rotational speed. To regenerate the desiccant wheel, to meet the electrical demand and also to provide heating load, prime mover in the form of internal combustion engine is selected. Since prime movers do not operate in the full load, their performance in the partial load has been evaluated. By taking into consideration the required regeneration heating, the number of required prime movers has been calculated based on the regeneration temperature and partial load for each city. The results show that the number of required prime movers is increased by increasing the regeneration temperature of the wheel.     

Combined monitoring and modeling indicate the most effective agricultural best management practices

Although water quality problems associated with agricultural nonpoint source (NPS) pollution have prompted the rapid and widespread adoption of a variety of so called "best management practices" (BMPs), there have been few realistic efforts to assess their combined effectiveness in reducing NPS pollution. This study used the Variable Source Loading Function (VSLF) model, a distributed watershed model, to simulate phosphorus (P) loading from an upstate New York dairy farm before and after the implementation of a suite of BMPs. With minimal calibration, the model calculates the dissolved P (DP) losses from impervious surfaces (e.g., barnyards), the plant/soil complex, field-applied manure, and loads associated with baseflow conditions. The simulated DP loads agreed well with measured loads for both the pre-BMP and post-BMP periods. More importantly, results showed that BMPs reduced DP loads by 35%, which is over half of the expected reduction if all manure was removed from the watershed, i.e., approximately 50% reduction. The model results indicate that had no BMPs been installed DP loads would be approximately 37% greater than observed at the watershed outlet. The most effective BMPs were those that disassociated pollutant loading areas from areas prone to generating runoff, i.e., hydrologically sensitive areas. By contrast, attempts to reduce P content in manure were somewhat less effective. This study demonstrates that a combination of distributed, mechanistic modeling and long-term monitoring provides better insights into the effectiveness of water quality protection efforts than either individually.     

Impacts of Alternative Manure Application Rates on Texas Animal Feeding Operations: A Macro Level Analysis1

Abstract: An integrated economic and environmental modeling system was developed for evaluating agro‐environmental policies and practices implemented on large scales. The modeling system, the Comprehensive Economic and Environmental Optimization Tool‐Macro Modeling System (CEEOT‐MMS), integrates the Farm‐level Economic Model (FEM) and the Agricultural Policy Environmental eXtender (APEX) model, as well as national databases and clustering and aggregation algorithms. Using micro simulations of statistically derived representative farms and subsequent aggregation of farm‐level results, a wide range of agricultural best management practices can be investigated within CEEOT‐MMS. In the present study, CEEOT‐MMS was used to evaluate the economic and water quality impacts of nitrogen (N) and phosphorus (P) based manure application rates when implemented on all animal feeding operations in the State of Texas. Results of the study indicate that edge‐of‐field total P losses can be reduced by about 0.8 kg/ha/year or 14% when manure applications are calibrated to supply all of the recommended crop P requirements from manure total P sources only, when compared to manure applications at the recommended crop N agronomic rate. Corresponding economic impacts are projected to average a US$4,800 annual cost increase per farm. Results are also presented by ecological subregion, farm type, and farm size categories.     

降低循环冷却水水耗的生产措施

为了有效降低循环冷却水系统补充水量、排污量，节约水处理剂的消耗、降低冷却水处理成本，关键在于提高循环水的浓缩倍数。本文对影响循环水浓缩倍数的因素进行了分析，总结了哈尔滨石化公司循环水场存在的问题。同时本文给出了提高循环水浓缩倍数的方法，上述方法能取得显著的环境效益和经济效益。     

Modeling of Low Temperature Geothermal District Heating Systems

Abstract

In this work, low temperature geothermal district heating systems with heat pumps have been studied and compared with fuel-oil boiler heating systems for intermittent and continuous regimes according to the optimum indoor air temperature and operational cost. Izmir Institute of Technology (IZTECH) Campus is taken as a case study. Various heat pump and boiler configurations are studied to meet required duty. Operational cost analysis for each alternative is conducted. According to the results, for IZTECH Campus the best alternative, which gives the optimum indoor air temperature and the lowest operational cost, is heat pump continuous regime.     

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.