Analysis of Effectiveness of Flexible Load Shifting Order on Optimum DSM
01 Dec 2017-
TL;DR: The energy management techniques presented in this paper is based on order of shifting of flexible loads which reduces the peak demand and improves the system reliability and makes the system sustainable.
Abstract: The problem of demand and supply balance is usually addressed at the generation side by addition of capacity to meet the load demand. With the recent trend in energy market, the problem of peak load demand can also be met from the user end. Hence, demand side management came into practice and it is one of the important aspect of microgrid through which the consumers can take an informed decision regarding their electricity consumption and benefits the energy providers in minimizing the peak demand by modifying the load profile. Load management is one of the most important components of demand side management to regulate the consumption of electricity. The energy management techniques presented in this paper is based on order of shifting of flexible loads. The flexible loads are shifted to obtain a flat load demand which in turn reduces the peak demand. Three different procedures are used for selecting which load to shift first and the methods are compared to decide most suitable one. The proposed method is supported with a case study of residential loads. This technique of load management improves the system reliability and makes the system sustainable.
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TL;DR: The importance of load management methodology (LM) is presented and helps in clarifying the difference between load management versus demand-side management (DSM), as LM is a crucial method of controlling the loads especially in peak periods, to prevent tripping.
9 citations
TL;DR: In this paper, the authors investigate how insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers) and find that the simulated results have a marked dependence on the data sampling rate.
Abstract: Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The potential improvement in the amount of self-consumption is usually estimated using smart meter and PV production data. Smart meter data are usually available only at sampling frequences far below the Nyquist limit. In this paper we investigate how this insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers). We base our analyses on measured consumption data from 16 households in the UK and corresponding PV data. We found that the simulated results have a marked dependence on the data sampling rate. The amount of self-consumed energy estimated with data sampled every 10 min was overestimated by 30–40% compared to estimations using data with 1 min sampling rate. We therefore recommend to take this factor into account when making predictions on the impact of appliance load shifting on the rate of self-consumption.
2 citations
Cites background from "Analysis of Effectiveness of Flexib..."
...A part of the usual NILM pipeline is in fact event detection (see, e.g., Reference [16])....
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...For cases where the voltage signal is present, the approach presented in Reference [17] could be used....
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...In Reference [11] the authors present a pre-processing step involving sorting the loads in either ascending or descending order before shifting....
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...A similar approach, with a different combinatorial search algorithm, is presented in Reference [13]....
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...A more specific load scenario is described in Reference [15], where the authors suggest shifting certain thermostat-controlled loads to enable demand response while keeping high comfort....
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01 Oct 2019
TL;DR: In this paper, two scenarios were conducted to determine whether load shifting methods using pump storage can save the cost of production of the electrical power system, and the results showed that using the pump storage as medium to load shifting method has proven to save 24 hours of electricity power production costs of $ 76.326,28 and electricity prices are also lower in $/kWh every hour.
Abstract: Electricity energy needs increase every year. If the load continues to increase and the supply of electricity is fixed, then one day it will cause the supply to be smaller than the demand. If electricity provider is able to provide electricity in accordance with the growth of expenses, then there will be an increase in the cost of production of the electric power system as well. Electricity production costs are different every hour, because it follows changes in load curve that changes every hour. This study aims to save electricity production costs for 1 day or 24 hours using load shifting methods with pump storage. The general definition of load shifting method is to move the peak load to the base load, so that the cost of generating electricity at peak loads becomes cheaper. Another way to move peak loads to basic loads can use pump storage. Indonesia has new Cisokan Pump Storage located in West Java. The working principle of pump storage is when the base load will act as pump to move water from the lower reservoir to the upper reservoir, and during this process it will consume electricity. When the peak load will act as generator by draining water from the upper reservoir to the lower reservoir, so that it will reduce electricity consumption during peak loads. In this study, two scenarios will be conducted to determine whether load shifting methods using pump storage can save the cost of production of the electrical power system. The first scenario is the operation of 24-hour electric power system without pump storage. The second scenario is the operation of the electrical power system for 24 hours using pump storage. The result of this study, for scenario 1 the total cost of production for 24 hours is $ 20.251.047,78. For scenario 2, the total production cost for 24 hours is $ 20.174.721,5. From scenario 1 and 2, using pump storage as medium to load shifting methods has proven to save 24 hours of electricity power production costs of $ 76.326,28 and electricity prices are also lower in $/kWh every hour.
1 citations
Cites background from "Analysis of Effectiveness of Flexib..."
...Load shifting, Valley Filling and Peak Clipping are included in the side management pattern [9,10,11,12]....
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References
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TL;DR: A heuristic-based Evolutionary Algorithm that easily adapts heuristics in the problem was developed for solving this minimization problem and results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
1,070 citations
"Analysis of Effectiveness of Flexib..." refers background in this paper
...Load profile that indicates the daily or seasonal demand curve is intelligently modified under demand management scheme and this is broadly classified into peak clipping, valley filling, load shifting, Strategic conservation, strategic load growth and flexible load shape [12]- [13]....
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TL;DR: This work improves the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles and simultaneously scheduling all DER schedules, to investigate the potential consumer value added by coordinated DER scheduling.
Abstract: We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.
824 citations
"Analysis of Effectiveness of Flexib..." refers background in this paper
...Load profile that indicates the daily or seasonal demand curve is intelligently modified under demand management scheme and this is broadly classified into peak clipping, valley filling, load shifting, Strategic conservation, strategic load growth and flexible load shape [12]- [13]....
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TL;DR: In this paper, the authors proposed an optimization algorithm to manage a virtual power plant (VPP) composed of a large number of customers with thermostatically controlled appliances based on a direct load control (DLC).
Abstract: In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability.
597 citations
"Analysis of Effectiveness of Flexib..." refers background in this paper
...Load management techniques for residential consumers can be classified into direct and indirect [4]- [5]....
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TL;DR: In this paper, the authors investigated the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 hours and achieved near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem.
Abstract: Interruptible loads represent highly valuable demand side resources within the electricity industry. However, maximizing their potential value in terms of system security and scheduling is a considerable challenge because of their widely varying and potentially complex operational characteristics. This paper investigates the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 h. The scheduling objective is to achieve a system requirement of total hourly curtailments while satisfying the operational constraints of the available interruptible loads, minimizing the total payment to them and minimizing the frequency of interruptions imposed upon them. This multiobjective optimization problem was simplified by using a single aggregate objective function. The BPSO algorithm proved capable of achieving near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem. The effectiveness of the approach was further improved by dividing the swarm into several subswarms. The proposed scheduling technique demonstrated useful performance for a relatively challenging scheduling task, and would seem to offer some potential advantages in scheduling significant numbers of widely varied and technically complex interruptible loads.
266 citations
"Analysis of Effectiveness of Flexib..." refers background in this paper
...In [7] and [8] authors have emphasized regarding the scheduling of appliances and learning their characteristics in a residential area....
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TL;DR: A demand side energy management for a grid connected household with a locally generated photovoltaic energy is proposed and to ensure efficient household energy management, smart scheduling of electrical appliances has also been presented.
Abstract: The advancement of renewable energy technologies has seen the emergence of customer owned grid tied wind and solar microgrids. These microgrids offer an opportunity to energy users to lower their energy costs as well as enabling the power suppliers to regulate the utility grid. However, the integration of the renewable energy based sources into the smart grid increases the complexity of the main grid. The success of this scheme will be heavily reliant on accurate real-time information exchange between the microgrid, the main grid, and the consumers. The communication between these agents will be critical in implementation of intelligent decisions by the smart grid. The microgrids will be required to relay energy forecasts information to the utility grid. Similarly, customers will be expected to submit energy demand schedules, to actively monitor energy price signals, to participate in energy bids, and to respond to energy management signals in real time. This kind of grid-user interaction will be overwhelming and could result in consumer apathy. There is therefore a need to develop smart systems that will autonomously execute all these tasks without the prompting of the customers. This paper presents one such approach. In this study, we proposed a demand side energy management for a grid connected household with a locally generated photovoltaic energy. To ensure efficient household energy management, smart scheduling of electrical appliances has also been presented.
247 citations
"Analysis of Effectiveness of Flexib..." refers methods in this paper
...These are mostly used for cost benefit analysis and load scheduling pattern [9]- [11]....
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