Journal of Energy Management and Technology
Volume:2 Issue: 2, Spring 2018

Abstract Energy conservation, clean environment are important topics for search. Heat Exchangers Network [HEN] is an effective way for achieving energy recovery and so on minimizing operating cost for chemical plants. Reduction of greenhouse gasses (GHG) emission is a direct result of energy integration while gathering it with clean fuel switching minimizes emissions. In this work we formulated a simultaneous methodology with a multiobjective function of minimizing cost and maximizing emission 'reduction through designing a [HEN] constrained by fuel type and minimum temperature difference approach (ΔTmin). Application of this methodology with a mathematical solver (GAMS) on an existing naphtha treating unit resulted excellent results. Energy recovery technique through HENs at several values of (ΔTmin) reduced energy consumption by 27% to 18% and reduced gasses emissions by 21% to 12% comparing to actual data of the treating unit. While application of fuel switching technique; increased emission reduction percentage to 34%. Combination both techniques improved results; where HEN at optimum (ΔTmin=18ᵒC) with natural gas switching achieved reduction of energy and GHG by 24% and 44% respectively, so it is the candidate design for the unit revamping. Another revamping technique was fuel switching to coke, where adding Post Combustion Carbon Capture (PCC) is an emission reduction solution by ≈ 85%.

Improving and enhancing methodologies for efficiently and effectively design of the energy systems is one of the most important challenges that energy engineers face. In this work, a multi-objective particle swarm optimization algorithm is applied for a highly constrained cogeneration problem named CGAM problem as a standard cycle to verify all optimization methods. The regarded objective functions are the exergetic efficiency that should be maximized and the total cost rate that should be minimized, simultaneously. In order to determine the polar effects of the pressure ratio and the turbine inlet temperature on the specified objective functions, a sensitivity analysis is performed. The related Pareto fronts with different values of equivalence ratios, unit costs of fuel and NOx emissions are represented and their effects on the system are studied. Furthermore, the comparison of the obtained results with those of other evolutionary algorithms demonstrates the superiority and efficiency of the considered multi-objective particle swarm optimization algorithm.

As regards the number of electric vehicles and their batteries energies vary in charging or discharging modes, the vehicle to grid technology can act as a variable load in charging mode or as a variable energy source in discharging mode. In this paper, a new approach is proposed. In the proposed approach, the control of the connection node voltage and the coordination of the charging and discharging of the EVs batteries are considered as the variable objective functions. The constraints are determined by several parameters such as the state of charge, connection node voltage, and charging-discharging time. Based on the proposed approach, the decision variables, which are the active and reactive powers exchanged between the EVs aggregator and the grid, are determined to achieve the defined objective functions. Reduction of grid losses in the peak load hours is the other advantage of the proposed approach. The simulations for a typical distribution system with V2G capabilities, based on the proposed approach, are carried out and tested for the different scenarios in charging and discharging modes. Finally, to lend credence to the proposed method, its results are compared with the results of the conventional method.

A retailer can sign multiple contracts with participation in demand response program (DRP). The energy sources considered for retailers include pool market and forward contracts. In this paper, several new DRP schemes are proposed for a retailer which is containing pool-order DR, forward DR and reward-base DR. proposed model is an agreement that retailer will participate it, if is useful. Pool market price uncertainty modeling is one of the main challenges in power system modeling which information gap decision theory (IGDT) is proposed for this uncertainty. In IGDT approach, the robustness and opportunity functions are used to study of different strategies in the presence of pool market price uncertainty. Robustness function is used in the risk-averse strategy while opportunity function is used in the risk-taker strategy. The proposed IGDT risk-constraint strategies of electricity retailer in presence of pool-order DR, forward DR and reward-base DR are modeled via mixed-integer non-linear programming which is solved using SBB solver under GAMS optimization software. To validate the proposed model, two cases are studied and positive effects of proposed DR scheme on the risk-averse, risk-neutral, risk-taker strategies are investigated, and the results are compared with each other.

Paint cure oven as one of the most important instruments in production lines involves with many key parameters like curing rate and energy consumption. Radiation paint cure ovens usually have smaller amount of energy consumption beside providing better curing conditions and as a result, attracts attentions of many manufacturers. Designing this type of ovens for curing paint of complicated geometries or thermally-sensitive materials is often a great inverse problem. Providing thermal conditions for the curing body to experience uniform cure all over its geometry without any zone of over-cured or under-cured is the most complicated part of the problem. Based upon previous works accomplished by the authors, in this study an optimization-based design method is presented in which the applied objective function is introduced based on equivalent isothermal temperature. It will be shown in this study that type and form of the objective function is the most principal issue in effectiveness and rate of the design process. Step sizes and direction vectors like other effective parameters in optimization process are studied in this article. Finally, the efficient method in designing curing ovens is employed for a typical geometry and evaluated. It will be shown that among the various considered methods, the Quasi-Newtonian method has halved the number of convergence steps and the differential step size has led to placement of more design points in the center of the cure window.

The generated power of wind turbines is extremely erratic owing to the intermittence nature of wind speed which can highly affect both the quality and the planning of power systems. Energy storage systems (ESSs) can provide a satisfactory solution for wind power applications by alleviating the harsh fluctuations pertaining to wind production and also providing ancillary services to the power system which in turn causes to increase the infiltration of wind power in the power systems. Previous studies represent that suitable location and size of ESS units in the distribution networks can bring many benefits such as peak shaving, loss reduction and reliability improvement. Under this context, this paper proposes a new risk-based method to determine optimal location and capacity of ESS units and wind turbines simultaneously. The proposed method is formulated as multi objective model which includes three monetary cost, technical risk and economic risk. In addition, the uncertainties considered in this problem include: (i) future load growth of system, (ii) wind generation and (iii) electricity market price. The aforementioned uncertainties are modeled using fuzzy numbers. The proposed optimization problem has been successfully solved using non-dominated sorting genetic algorithm (NSGA-II) and eventually, a “max-min” approach is employed to select the best solution among the obtained Pareto optimal set. The numerical studies performed on the 9-node and 33-node distribution systems indicate the advantages and sufficiency of the proposed methodology.

Renewable energy systemswill bewidespread power sources in future years due to their sustainability and clean nature.Due to intermittent nature of many renewable energy resources (such as wind, photovoltaic and etc.),their hybrid usage are preferred. One of the most important issues related to hybrid renewable energy systemsis finding the optimal size of their parts to utilize them efficiently and economically. There are several methods for optimal sizing of hybrid renewable energy systems, reported in many articles, containing their own merits and demerits.The current paper reviewed such methodologies and compared them using some appropriate indicators. This paper helps the system designers to select the appropriate sizing method for their hybrid renewable energy systems. Nowadays electrical energy is one of the most necessary requirements of mankind and is a vital factor for social and economic developments [1-3].Due to increase in population, fast urbanization, rapid industrialization and increased energy consumption, the demand for electricity is increasing.