Optimal Generation Rescheduling in Microgrids Under Uncertainty (original) (raw)
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2021
This paper proposes a multi-objective function for hourly optimization of microgrids performance through minimizing the operating costs, losses and, voltage deviation index. A fuzzy decision-maker is used in this study to select the best solution from the optimally set goals by the Pareto Front beam method. In this paper, the collections of microgrids are separated into several clusters and power transaction between clusters as well as between distribution system and MG clusters, with consideration for the uncertainty of renewable energy resources (RESs), is examined. An hourly robust energy management approach is presented for distribution systems under the penetration of renewable energy-based MGs. In addition to wind turbines and photovoltaics as RESs, the MGs are equipped with energy storage systems and micro-turbines. The uncertainty of renewable generation is demonstrated via the information gap decision theory (IGDT) technique. To validate the effectiveness of the proposed mo...
A fuzzy-based approach for microgrids islanded operation
Electric Power Systems Research, 2017
Power system blackouts harm economic activities and worsen the customers' welfare. Smart grids' selfhealing capacity is an important feature for future power systems and it should also include the ability to manage the distributed energy resources to ensure power supply for a longer time. This is required because the duration of a blackout is unknown and bulk power system blackstart is a complex task. The deployment of microgrids can overcome these challenges since they may be operated in an autonomous way. This paper proposes a methodology for microgrid management in islanded conditions aiming to maximize the duration of power supply taking into account the availability of renewable sources and stored energy. In order to accomplish this goal, some management options are considered, such as load shedding, dispatch of expensive fossil fuel sources, and demand response actions. The control actions are determined with the help of a fuzzy logic methodology. The proposed approach is validated with a modified IEEE 34 node sample system.
Power demand and supply management in microgrids with uncertainties of renewable energies
International Journal of Electrical Power & Energy Systems, 2014
An important task of power demand and supply management in microgrids is to maintain a good match between power generation and consumption at the minimum cost. Since the highly fluctuant renewable energies constitute a significant portion of the power resources in microgrids, the microgrid system central controller (MGCC) faces the challenge of effectively utilizing the renewable energies while fulfilling the requirements of customers. To tackle the problem, a novel power demand and supply management scheme is proposed in this paper, which mainly includes three parts as follows. Firstly, a novel uncertainty model is developed to capture the randomness of renewable energy generation which, by introducing a reference distribution according to past observations and empirical knowledge and defining a distribution uncertainty set to confine the uncertainty of renewable energies, allows the renewable energies to fluctuate around the reference distribution. An optimization problem is then formulated to determine the optimal power consumption and generation scheduling for minimizing the fuel cost. Finally, a two-stage optimization approach is proposed to transform and then solve the prime problem. Numerical results indicate that the proposed scheme helps effectively reduce the energy cost. Detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for policy making for the future MGCC.
IEEE Access
This article deals with the stochastic scheduling of a microgrid (MG) to balance the economical and resilience metrics. In the proposed model, the MG resilience indices are integrated into the economic criteria to ensure the resilience of MG operation alongside the main MG actors' profit/loss. The MG fragility index, recovery efficiency index, MG voltage index, and lost load index are considered in the proposed planning model. Further, to make the results more realistic, the uncertainties associated with energy price and wind production, alongside with planning of energy storage systems and electric vehicles parking lots are considered. To achieve a better solution for the security-constraint operation of MG, AC network equations are included in the system modeling. Finally, a large-scale MG based on the IEEE-33 bus testbed is utilized to evaluate the effectiveness of the proposed stochastic scheduling program.
Electronics
A bi-level operation scheduling of distribution system operator (DSO) and multi-microgrids (MMGs) considering both the wholesale market and retail market is presented in this paper. To this end, the upper-level optimization problem minimizes the total costs from DSO’s point of view, while the profits of microgrids (MGs) are maximized in the lower-level optimization problem. Besides, a scenario-based stochastic programming framework using the heuristic moment matching (HMM) method is developed to tackle the uncertain nature of the problem. In this regard, the HMM technique is employed to model the scenario matrix with a reduced number of scenarios, which is effectively suitable to achieve the correlations among uncertainties. In order to solve the proposed non-linear bi-level model, Karush–Kuhn–Tucker (KKT) optimality conditions and linearization techniques are employed to transform the bi-level problem into a single-level mixed-integer linear programming (MILP) optimization problem....
Robust optimal operation of smart distribution grids with renewable based generators
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components' failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. Robust operation of these systems is effectively taken into consideration to manage the uncertainty, which offers an explicit way to control the desired conservativeness. This paper presents an optimal operating program for smart grids equipped with wind generators, controllable distributed generators, energy storage systems, and reactive power compensators. In order to make the studies more practical, uncertainty about wind generators and grid loads is taken into account. Furthermore, the presented operating program is robust in various conditions, i.e. there is no need to change the operating program in a wide range of probable states. The point estimation method and fuzzy clustering method are used for probabilistic assessment of the distribution system in the presence of uncertainties. The IEEE 37-node standard test system, which is a highly unbalanced system, is selected for the case study and the results are discussed comprehensively.
IET Renewable Power Generation, 2018
Uncertain natures of the renewable energy resources and consumers' participation in demand response (DR) programs have introduced new challenges to the energy and reserve scheduling of microgrids, particularly in the autonomous mode. In this paper, a risk-constrained stochastic framework is presented to maximize the expected profit of a microgrid operator under uncertainties of renewable resources, demand load and electricity price. In the proposed model, the trade-off between maximizing the operator's expected profit and the risk of getting low profits in undesired scenarios is modeled by using conditional value at risk (CVaR) method. The influence of consumers' participation in DR programs and their emergency load shedding for different values of lost load (VOLL) are then investigated on the expected profit of operator, CVaR, expected energy not served (EENS) and scheduled reserves of microgrid. Moreover, the impacts of different VOLL and risk aversion parameter are illustrated on the system reliability. Extensive simulation results are also presented to illustrate the impact of risk aversion on system security issues with and without DR. Numerical results demonstrate the advantages of customers' participation in DR program on the expected profit of the microgrid operator and the reliability indices. Nomenclature Indices (.).,t,s At time t in scenario s. i,w,v,j Indices of DGs, wind turbines, PV units and group of customers. t,s Indices of time slots and scenarios. b, n, r Indices of system buses.
2010 IEEE International Energy Conference, 2010
Economic dispatch (ED) of a grid connected and renewable integrated microgrid system is considered in this paper. Two wind farms take the renewable energy sources (RES) into consideration. A parameter worst-case-transaction-cost which arises due to the stochastic availability and uncontrollable nature of wind farms is also emphasised and efforts have been taken to minimize it too. Hence the paper's focus into split objective functions and the generation costs and the worst case transaction costs are optimised separately and also the net microgrid cost is optimized as a whole. Two different cases with highly varying transaction prices are studied. Two meta-heuristic soft computing algorithms are applied for optimization and a comparative analysis among them is studied. Numerical results are tabulated to justify the effectiveness of the novel approach.
Robust operation of microgrid energy system under uncertainties and demand response program
Indonesian Journal of Electrical Engineering and Computer Science
Microgrid energy systems are one of suitable solutions to the available problems in power systems such as energy losses, and resiliency issues. Local generation by these energy systems can reduce the role of the upstream network, which is a challenge in risky conditions. Also, uncertain behavior of electricity consumers and generating units can make the optimization problems sophisticated. So, uncertainty modeling seems to be necessary. In this paper, in order to model the uncertainty of generation of photovoltaic systems, a scenario-based model is used, while the robust optimization method is used to study the uncertainty of load. Moreover, the stochastic scheduling is performed to model the uncertain nature of renewable generation units. Time-of–use rates of demand response program (DRP) is also utilized to improve the system economic performance in different operating conditions. Studied problem is modeled using a mixed-integer linear programming (MILP). The general algebraic mod...
Optimal reconfiguration and supply restoration of distribution networks with hybrid microgrids
Electric Power Systems Research, 2020
Hybrid microgrids (HMGs) typically combine renewable-based and conventional generation with energy storage and controllable loads, offering a number of benefits besides simple reduction of CO2 emissions. If properly sized and controlled, such HMGs could be operated as fully dispatchable parts of a wider distribution network and could significantly improve overall system reliability and reduce impact of outages on the connected customers. Designing and operating HMGs, however, is a complex task and this paper presents an approach based on improved binary genetic algorithm (IBGA) method for optimal dispatching and control of all HMG resources: renewable and conventional generators, energy storage system and demand-manageable loads. Following an outage and based on available HMG resources, the IBGA aims to find a solution for network reconfiguration in which radial structure of the network is maintained and loads are supplied in accordance to a specified priority list, while also satisfying operational constraints: bus voltage and branch loading limits. The presented methodology is illustrated and validated on a commonly used IEEE 33-bus test network, where obtained results demonstrate that HMGs can improve overall system reliability and reduce negative impact of outages on the connected customers.