Demand side management in a smart micro-grid in the presence of renewable generation and demand response
TL;DR: In this paper, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources.
About: This article is published in Energy.The article was published on 2017-05-01. It has received 223 citations till now. The article focuses on the topics: Demand response & Smart grid.
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TL;DR: In this article, a microgrid system that consists of photovoltaic, wind turbine generator, electric storage system and diesel generator is implemented to test their commercial prospects in rural communities that have no access to electricity due to economic and technical constraints.
260 citations
TL;DR: The results show that with effective interactions among participants, electricity consumption of the entire energy system closely tracks the generation pattern of renewable resources, resulting in a significantly flattened schedule of grid electricity procurement.
172 citations
TL;DR: The potential benefits derived by implementing DSM in electrical power networks are presented and an extensive literature survey on the impacts of DSM on the reliability of electrical power systems is provided for the first time.
Abstract: Electricity demand has grown over the past few years and will continue to grow in the future. The increase in electricity demand is mainly due to industrialization and the shift from a conventional to a smart-grid paradigm. The number of microgrids, renewable energy sources, plug-in electric vehicles and energy storage systems have also risen in recent years. As a result, future electricity grids have to be revamped and adapt to increasing load levels. Thus, new complications associated with future electrical power systems and technologies must be considered. Demand-side management (DSM) programs offer promising solutions to these issues and can considerably improve the reliability and financial performances of electrical power systems. This paper presents a review of various initiatives, techniques, impacts and recent developments of the DSM of electrical power systems. The potential benefits derived by implementing DSM in electrical power networks are presented. An extensive literature survey on the impacts of DSM on the reliability of electrical power systems is also provided for the first time. The research gaps within the broad field of DSM are also identified to provide directions for future work.
137 citations
Cites background from "Demand side management in a smart m..."
...A probabilistic programming for smart microgrids has been implemented by considering demand response as compensation for the uncertainty caused by wind and solar power generation [62]....
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TL;DR: The results show that it is increasingly important to focus on the short-term system operations in the planning models integrating variable renewables, specially the constraints of flexible generation, interregional transmission as well as energy storage and demand side response.
136 citations
TL;DR: A two-stage real-time DSM method for an MG including different time scales, integrated with schedulable ability (SA) and uncertainties, is proposed and Numerical simulations on a residential MG show the reasonableness and effectiveness of the proposed method.
Abstract: Different from most existing studies that focus on offline demand side management (DSM) in microgrids (MGs) while neglecting forecasting errors of uncertain renewable generations, this paper studies online DSM. A two-stage real-time DSM method for an MG including different time scales, integrated with schedulable ability (SA) and uncertainties, is proposed. In the first stage, a novel internal pricing model is developed. On this basis, a model predictive control-based dynamic optimization is applied to minimize the operation cost and maintain the power balance considering the uncertainties imposed by both supply and demand sides in the MG system. In the second stage, we define the concept of SA for response executors (REs) and also establish an SA evaluation system taking the real-time and history information of the REs into account. In doing so, a faster-time scale online power allocation among REs is carried out in the framework of dynamic optimization to further compensate for the uncertainties in real-time, based on the evaluated SA values of the REs and the required compensation power. Numerical simulations on a residential MG show the reasonableness and effectiveness of the proposed method.
116 citations
Cites methods from "Demand side management in a smart m..."
...In [12]–[14], probabilistic stochastic programming methods were exploited to address the uncertainties....
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References
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TL;DR: In this article, the authors proposed a coordinated charging strategy to minimize the power losses and to maximize the main grid load factor of the plug-in hybrid electric vehicles (PHEVs).
Abstract: Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay This uncoordinated power consumption on a local scale can lead to grid problems Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses As the exact forecasting of household loads is not possible, stochastic programming is introduced Two main techniques are analyzed: quadratic and dynamic programming
2,601 citations
"Demand side management in a smart m..." refers background in this paper
...In a typical system, including a battery with a capacity of 30kWh, minimum andmaximum charges are considered to be 10% and 100% of the battery capacity, respectively, with a charge and discharge efficiency of 94% [39,40]....
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12 May 2002
TL;DR: This paper introduces a proposal to extend the heuristic called "particle swarm optimization" (PSO) to deal with multiobjective optimization problems and it maintains previously found nondominated vectors in a global repository that is later used by other particles to guide their own flight.
Abstract: This paper introduces a proposal to extend the heuristic called "particle swarm optimization" (PSO) to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and it maintains previously found nondominated vectors in a global repository that is later used by other particles to guide their own flight. The approach is validated using several standard test functions from the specialized literature. Our results indicate that our approach is highly competitive with current evolutionary multiobjective optimization techniques.
1,842 citations
TL;DR: In this article, the authors present a summary of demand response in deregulated electricity markets and highlight the most common indices used for DR measurement and evaluation, and some utilities' experiences with different demand response programs are discussed.
1,751 citations
"Demand side management in a smart m..." refers background in this paper
...One of these solutions is the use of Demand Response Programs (DRPs) [9,10]....
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TL;DR: In this article, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss.
Abstract: It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, solar thermal systems, biomass, and various forms of hydraulic power. In this paper, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer nonlinear programming (MINLP), with an objective function for minimizing the system's annual energy losses. The constraints include the voltage limits, the feeders' capacity, the maximum penetration limit, and the discrete size of the available DG units. This proposed technique has been applied to a typical rural distribution system with different scenarios, including all possible combinations of the renewable DG units. The results show that a significant reduction in annual energy losses is achieved for all the proposed scenarios.
1,243 citations
TL;DR: A determinist energy management system for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine is proposed, which is implemented in two parts: a central energy management of the microgrid and a local power management at the customer side.
Abstract: The development of energy management tools for next-generation PhotoVoltaic (PV) installations, including storage units, provides flexibility to distribution system operators. In this paper, the aggregation and implementation of these determinist energy management methods for business customers in a microgrid power system are presented. This paper proposes a determinist energy management system for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine. The system is organized according to different functions and is implemented in two parts: a central energy management of the microgrid and a local power management at the customer side. The power planning is designed according to the prediction for PV power production and the load forecasting. The central and local management systems exchange data and order through a communication network. According to received grid power references, additional functions are also designed to manage locally the power flows between the various sources. Application to the case of a hybrid supercapacitor battery-based PV active generator is presented.
905 citations