Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies

Renewable-energy-based microgrids (MGs) are being advocated around the world in response to increasing energy demand, high levels of greenhouse gas (GHG) emissions, energy losses, and the depletion of conventional energy resources. However, the high investment cost of the MGs besides the low selling price of the energy to the main grid are two main challenges to realize the MGs in developing countries such as Iran. For this reason, the government should define some incentive policies to attract investor attention to MGs. This paper aims to develop a framework for the optimal planning of a renewable energy-based MG considering the incentive policies. To investigate the effect of the incentive policies on the planning formulation, three different policies are introduced in a pilot system in Iran. The minimum penetration rates of the RESs in the MG to receive the government incentive are defined as 20% and 40% in two different scenarios. The results show that the proposed incentive policies reduce the MG’s total net present cost (NPC) and the amount of carbon dioxide (CO2) emissions. The maximum NPC and CO2 reduction in comparison with the base case (with incentive policies) are 22.87% and 56.13%, respectively. The simulations are conducted using the hybrid optimization model for electric renewables (HOMER) software.João Soares has received funding from FCT, namely CEECIND/02814/2017 and UIDB/00760/2020.info:eu-repo/semantics/publishedVersio

A Risk-Based Decision Framework for the Distribution Company in Mutual Interaction with the Wholesale Day-ahead Market and Microgrids

One of the emergent prospects for active distribution networks ( DN ) is to establish new roles to the distribution company ( DISCO ). The DISCO can act as an aggregator of the resources existing in the DN , also when parts of the network are structured and managed as microgrids ( MG s). The new roles of the DISCO may open the participation of the DISCO as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the DISCO are addressed by proposing a bilevel optimization approach in which the DISCO problem is modeled as the upper-level problem and the MG s problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the DISCO 's risk aversion is modeled by using the conditional value at risk. The resulting nonlinear bilevel model is transformed into a linear single-level one by applying the Karush–Kuhn–Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus DN connected to the IEEE RTS 24-bus power system

Modeling Operation Problem of Micro-grids Considering Economical, Technical and Environmental issues as Mixed-Integer Non-Linear Programming

Reduction of fossil resources, increasing the production of greenhouse gas emissions and demand growth lead to greater use of distributed energy resources in power system especially in distribution networks. Integrating these resources in order to supply local loads creates a new concept called micro-grid. Optimal operation of micro-grid in the specific time period is one of the most important problems of them. In this paper, the operation problem of micro-grids is modeled considering the economical, technical and environmental issues, as well as uncertainties related to loads, wind speed and solar radiation. The resulting model is a Mixed-Integer Non-Linear Programming (MINLP). To demonstrate the effectiveness of the proposed model, Bisheh village in Iran is considered as a case study. The results showed that considering load curtailment costs, the power losses of the main grid, the penalties of pollutant gasses emissions and the elimination of energy subsides will tremendous impacts on the operation of microgrids. Article History: Received March 12, 2016; Received in revised form June 20, 2016; Accepted July 2nd 2016; Available online How to Cite This Article: Salahi, S., and Bahramara, S. (2016) Modeling Operation Problem of Micro-grids Considering Economical, Technical and Environmental issues as Mixed-Integer Non-Linear Programming. Int. Journal of Renewable Energy Development, 5(2), 139-149. http://dx.doi.org/10.14710/ijred.5.2.139-149 </p

Cloud energy storage in power systems: Concept, applications, and technical challenges

Abstract Cloud energy storage (CES) in the power systems is a novel idea for the consumers to get rid of the expensive distributed energy storages (DESs) and to move to using a cloud service centre as a virtual capacity. Although the different characteristics and applications of the energy storages are reviewed in some papers, there is no review study on the CES concepts, formulations, applications, and challenges. Therefore, the main contribution of this paper is to review the applications of the CES and its technical challenges in the power systems. For this purpose, the concept and fundamentals of the CES, as well as their role in supporting the consumers and the power network, are described first. The flow of information in a CES is then discussed, and the roles of the operator, consumers, and facilities, as the main sectors of the CES are explained. The existing studies are classified and discussed regarding the different applications of the CES in the power systems and their drawbacks are highlighted. The operation and planning (feasibility) problems of the CES are investigated. Reviewing the existing studies shows that comprehensive models are required to address the energy management (EM) and feasibility analysis of the CES applications. To address this challenge, the general formulations are presented for the planning and the operation scheduling problems of the CES. In addition, addressing different CES applications in the power systems leads to some technical challenges which are described. Finally, future directions are suggested for potential researchers to continue the studies on the CES integration and application

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

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Day-ahead self-scheduling from risk-averse microgrid operators to provide reserves and flexible ramping ancillary services

In this paper, a new decision-making framework is proposed for the day-ahead self-scheduling problem of microgrids, in which the microgrid operator (MGO) can provide ancillary services for both the independent system operator (ISO) and the distribution system operator (DSO). The MGO provides the reserve and flexible ramping product (FRP) services for the ISO through participating in the corresponding markets. Also, the MGO reschedules its resources to provide the requested services for the DSO. To model the uncertain behavior of the renewable energy resource, demand, and real-time energy price, the MGO problem is modeled as a two-stage stochastic model. Then, the uncertainties of the reserve and the FRP deployment in real-time operation are modeled using the information gap decision theory approach. The results show the effects of the different strategies in scheduling the local resources adopted by the MGO to participate in the energy and ancillary service markets. In the risk-based model proposed for the MGO, increasing the risk parameter decreases the capacity of the provided reserve and ramp-up FRP while it increases the energy sold to the day-ahead energy market

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

In this study, multi-objective optimal scheduling of smart energy Hub system (SEHS) in the day ahead is proposed. A SEHS is comprising of interconnected energy hybrid system infrastructures such as electrical, thermal, wind, solar, natural gas and other fuels to supply many types of electrical and thermal loads in a two-way communication platform. All objectives in this paper, are minimized and consist of 1) operation cost and emission polluting in generation side, 2) loss of energy supply probability (LESP) in demand side, and 3) deviation of electrical and thermal loads with the optimal level of electrical and thermal profile in the day ahead. The third objective to flatten electrical and thermal demand profile using Demand Side Management (DSM) by the optimal shifting of electrical and thermal shiftable loads (SLs) is proposed. Also, stochastic modelling of renewable energy sources (RESs) and electrical and thermal loads by Monte Carlo technique is modelled. Using GAMS optimization software, proposed approach by ε -constraint method for obtaining to non-dominated Pareto solutions of objectives is implemented. Moreover, by the decision-making method, the best solution of non-dominated Pareto solutions is selected. Finally, two case studies and sensitivity analysis in case studies for confirmation of the proposed approach are analysed

Robust bi-level risk-based optimal scheduling of microgrid operation against uncertainty

The model introduced in this paper is the first to propose a decentralized robust optimal scheduling of MG operation under uncertainty and risk. The power trading of the MG with the main grid is the first stage variable and power generation of DGs and power charging/discharging of the battery are the second stage variables. The uncertain term of the initial objective function is transformed into a constraint using robust optimization approach. Addressing the Decision Maker’s (DMs) risk aversion level through Conditional Value at Risk (CVaR) leads to a bi-level programming problem using a data-driven approach. The model is then transformed into a robust single-level using Karush–Kahn–Tucker (KKT) conditions. To investigate the effectiveness of the model and its solution methodology, it is applied on a MG. The results clearly demonstrate the robustness of the model and indicate a strong almost linear relationship between cost and the DMs various levels of risk aversion. The analysis also outlines original characterization of the cost and the MGs behavior using three well-known goodness-of-fit tests on various Probability Distribution Functions (PDFs), Beta, Gumbel Max, Normal, Weibull, and Cauchy. The Gumbel Max and Normal PDFs, respectively, exhibit the most promising goodness-of-fit for the cost, while the power purchased from the grid are well fitted by Weibull, Beta, and Normal PDFs, respectively. At the same time, the power sold to the grid is well fitted by the Cauchy PDF