Demand Side Management Strategies Research Articles

Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid

Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM) and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP), and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL), are still imperfect, which make it difficult for the active distribution network (ADN) to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.

A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack

Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.

Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations

Abstract This paper presents a smart Transactive energy (TE) framework in which home microgrids (H-MGs) can collaborate with each other in a multiple H-MG system by forming coalitions for gaining competitiveness in the market. Profit allocation due to coalition between H-MGs is an important issue for ensuring the optimal use of installed resources in the whole multiple H-MG system. In addition, considering demand fluctuations, energy production based on renewable resources in the multiple H-MG can be accomplished by demand-side management strategies that try to establish mechanisms to allow for a flatter demand curve. In this regard, demand shifting potential can be tapped through shifting certain amounts of energy demand from some time periods to others with lower expected demand, typically to match price values and to ensure that existing generation will be economically sufficient. It is also possible to obtain the maximum profit with the coalition formation. In essence the impact of the consumption shifting in the multiple H-MG schedule can be considered while conducting both individual and coalition operations. A comprehensive simulation study is carried out to reveal the effectiveness of the proposed method in lowering the market clearing price (MCP) for about 15% of the time intervals, increasing H-MG responsive load consumption by a factor of 30%, and promoting local generation by a factor of three. The numerical results also show the capability of the proposed algorithm to encourage market participation and improve profit for all participants.

A multi-agent system for optimal sizing of a cooperative self-sustainable multi-carrier microgrid

In this paper, an interactive multi-agent system (MAS) is applied to the problem of optimal sizing of a cooperative self-sustainable multi-carrier microgrid that includes various privately-owned entities. The proposed microgrid includes photovoltaic (PV) arrays, batteries, an electrolyzer, a hydrogen tank, a fuel cell (FC), a reactor-reformer system, a hydrogen compressor-dispenser system, a converter, residential electrical loads, and a charging/refilling station. The proposed MAS enables information exchange required for the application of demand-side management (DSM) strategy and has five agents, namely generation agent (GA), electrical load agent (LA), charging/refilling station agent (SA), control agent (CA), and design agent (DA). The GA is responsible for managing the distributed energy resources of the microgrid. The LA aggregates the residential electrical loads. The SA is responsible for charging of plug-in hybrid electric vehicles (PHEVs) and refilling of fuel cell electric vehicles (FCEVs). The CA coordinates the interactions between the field level agents. The DA finds the optimal sizes of the system’s components by minimizing the total cost of the system through particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed system can reduce the overall cost of the microgrid in comparison with non-interactive methods.

Use of weather forecast for increasing the self-consumption rate of home solar systems: An Italian case study

With the aim of increasing the self-consumption rate of grid-connected Photovoltaic (PV) home systems, two main options can be implemented: the inclusion of an energy storage system, in particular a battery bank, and the adoption of a Demand Side Management (DSM) strategy. However, both the reshaping of the load consumption curve with the displacement of deferrable loads and the optimal management of the battery bank require estimation of the daily PV generation profile. The assessment of the on-site energy production can be carried out based on weather forecast data. However, the latter are characterized by uncertainty, which may affect the achievable self-consumption rate.This work investigates the influence of weather forecast errors on the performance of home PV systems equipped with a battery bank and characterized by a certain share of deferrable loads. Two different weather forecast services are considered, referring to the annual meteorological conditions occurring in Rome, and energy consumption data for 150 different households are analysed. The self-consumption rate is maximized by solving a suitable optimization problem, while different combinations of relative battery capacity, PV-to-load ratio and share of deferrable loads are considered. Two different approaches—deterministic and stochastic—are adopted and compared with an ideal approach where the PV generation profile is perfectly forecasted. The results show that the adoption of the deterministic approach leads to a reduction in the achievable self-consumption rate in the range of 0.5–4.5% compared to the ideal approach. The adoption of a stochastic approach further reduces the deviations from the ideal case, especially in the case of consumption profiles with a high share of deferrable loads. Finally, a preliminary economic analysis proves that the use of a battery bank is not yet a cost-effective solution and a price reduction of the current battery prices is therefore required.

Dynamic demand response in smart buildings using an intelligent residential load management system

In a smart grid environment, the utilities, which electric power distribution companies with different pricing strategies, are interested in demand side management (DSM) schemes to achieve reduction in peak demand. Utilities provide additional benefits and encourage the consumers to have direct interaction with the grid by actively participating in DSM programs. An intelligent residential load management system (IRLMS) is proposed for consumers to attain reduction in electricity bill and avail more incentive from the utility by responding to their DSM schemes. The IRLMS attempts to reduce the electricity bill and maintain the total load under maximum demand limit by scheduling the schedulable loads while considering the operating dynamics of non-schedulable loads. The IRLMS scheduler is built with two different techniques, namely priority-based scheduling algorithm and optimisation-based scheduling algorithm for different utility tariff methods such as flat rate tariff and real time pricing, respectively. The IRLMS model is implemented in MATLAB and the results illustrate significant benefits for both consumers and utility.

Evaluation Of The Potential Of Electric Storage Using Decentralized Demand Side Management Algorithms

In this paper, we report the results of simulations of a demand side management strategy, developed within the context of the GridSense project. GridSense is a multi-objective energy management system that aims at decreasing both the end user energy costs and the congestions on the local feeder. The algorithm controls single appliances, among which boilers, heat pumps, EV chargers and stationary batteries. The control strategy is completely decentralized and does not require communication outside the building. A multiphysics simulation framework, which allows simulating the electrical grid, buildings and appliances, has been implemented using the Modelica language, while the control algorithms are coded in C. The performance of the proposed solution against the two main optimization objectives (cost and grid friendliness) has been tested in simulation under different scenarios. We systematically varied the capacity and E-rate of the energy storage. Our analysis shows that savings are mostly dependent on the battery capacity, while being only slightly affected by the battery inverter-charger size. The break even system price of the different solutions was calculated for an example house in a typical Swiss tariff scheme.

Demand side management perspective on the interaction between a non-ideal grid and residential LED lamps

Lighting industries across the world are witnessing a rapid shift towards energy efficient LED (Light Emitting Diode) lightings. These lighting technologies can be seen asan alternative tool to the DSM (Demand Side Management) in residential, commercial as well as industrial sector. DSM has a strong linkage with the reliability and quality of power supply and any variations in supply conditions may significantly affect DSM strategies. As LED lighting technologies are penetrating the lighting market at a rapid pace, it is important to assess the different impacts of distribution grid conditions on the power quality and energy consumption performance of LEDs. This paper presents an extensive analysis of the consequences of sustained abnormal voltage, supply voltage harmonics, and supply frequency variations on LED lamps and its overall effect on the popular “LED lighting DSM Program” – being adopted worldwide. It was observed that under the variable grid conditions, some lamps’ technologies offer DSM like advantages without being actively engaged in DSM. Several parameters have been evaluated, analyzed and, recommendations have been made for LED-based passive DSM strategies which may encompass the LED manufacturers, the utilities, and the governments.

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.

Demand side management approach to rural electrification of different climate zones in Indian state of Tamil Nadu

This paper involves the hybrid power potential implementation in view of six climatic zones in Indian state of Tamil Nadu. An intertwined techno-economic feasible study and energy management analysis of Hybrid Renewable Energy System (HRES) has been proposed to cater to need of the electrical energy requirement in un-electrified village hamlets of Tamil Nadu. The HRES feasibility, size optimization, cost and sensitivity analyses are performed to satisfy the electrical energy requirements of the considered area. A combination of Demand Side Management (DSM) and optimum tilt solar panel approach has also been analyzed through HOMER Energy® simulation. The selection of HRES configuration is based on real-time data collected from six different climatic zones. The optimization results of the considered system are presented and compared with and without DSM strategy. The optimum planning of HRES is based on ranking scheme which includes technical and ecological aspects for sustainable development. In addition, to evaluate the most feasible consideration of the system, sensitivity analysis has been performed upon the load variation, biomass and diesel price too. The simulation results of the proposed HRES configuration can improve the renewable fraction and offer more employment opportunities to the local people, compared to the existing PV-DG-Battery HRES.

Australian Power Generation Technology APGT

The Australian Power Generation Technology (APGT) APGT delivers a technology neutral review of major large scale commercial power generation technologies for Australia. The technologies examined, among others, include: Carbon capture, transport and storage with key findings including:•To facilitate the implementation of CCS in Australia, one or more CO2 transport and storage networks need to be developed by 2030.•The cost for transport and storage of CO2 (excluding owner's and risk adjusted costs) from power plants in Australia is likely to vary from $5– 14/t CO2 to almost $70/t CO2.•Variations in factors such as operating conditions, engineering assumptions, material costs, topography and geological characteristics may lead to different costs. The integrated design of capture systems, transport routes, operating conditions and injection strategies may lead to lower costs.Learnings across all new power generation technologies including CCS will decrease costs to 2030.Intermittent renewables, energy storage systems, demand-side management schemes and high volume - high availability technologies such as coal and gas with CCS are likely. These technologies, working together will provide reliable electricity, 24/7 with a low environmental impact and an optimum overall cost of electricity.Through the levelised cost of electricity (LCOE), it is evident that a change of thinking around the cost of CCS needs to evolve. Consideration needs to be given to financing and policy options by government into the near future if CCS is to become a near-term reality. Furthermore, like current electricity grids, grids in the future will use a diverse mix of technologies as all technologies have arrange of more favourable and less favourable characteristics.

Demand side management with consumer clusters in cyber‐physical smart distribution system considering price‐based and reward‐based scheduling programs

This study presents a demand side management (DSM) strategy for a cyber-physical smart distribution system (CPSDS). The proposed approach uses the price-based as well as reward-based DSM schemes as a part dual objective function. The objectives of the proposed scheduling approach comprise the profit maximisation objectives of demand response aggregator agent (DRAA) and network performance objectives of the distribution system operation agent. The same are achieved by providing incentives to the participating customers. The incentive information is communicated to responsive load agent (RLA) using cyber infrastructure (communication channels, sensors and cloud storage systems) and thus allowing customers to select the incentive program of their own choice as per their flexibility. The real-time implementation of the program is considered to have direct load control based once the event trigger acknowledgement is received by DRAA/utility from RLA for control action on responsive loads. The proposed price-based and reward-based DSM framework in CPSDS is evaluated using IEEE 37 bus test system. The simulation results of proposed dual objective price-based and reward-based mechanism are presented, discussed and compared with single objective price-based approach. The same demonstrates the improved tradeoff between techno-economic aspects of distribution system operation.

Energy implications of the millennium drought on urban water cycles in Southeast Australian cities

Abstract During the Millennium Drought in Australia, a wide range of supply-side and demand-side water management strategies were adopted in major southeast Australian cities. This study undertakes a time-series quantification (2001–2014) and comparative analysis of the energy use of the urban water supply systems and sewage systems in Melbourne and Sydney before, during and after the drought, and evaluates the energy implications of the drought and the implemented strategies. In addition, the energy implications of residential water use in Melbourne are estimated. The research highlights that large-scale adoption of water conservation strategies can have different impacts on energy use in different parts of the urban water cycle. In Melbourne, the per capita water-related energy use reduction in households related to showering and clothes-washing alone (46% reduction, 580 kWhth/p/yr) was far more substantial than that in the water supply system (32% reduction, 18 kWhth/p/yr). This historical case also demonstrates the importance of balancing supply- and demand-side strategies in managing long-term water security and related energy use. The significant energy saving in water supply systems and households from water conservation can offset the additional energy use from operating energy-intensive supply options such as inter-basin water transfers and seawater desalination during dry years.

Demand-Side Management System for Autonomous DC Microgrid for Building

This paper proposes a new demand-side management (DSM) scheme for the autonomous DC microgrid for the future building. The DC distribution system is considered as a prospective system due to the increase of DC loads and DC power sources such as photovoltaic (PV), and battery bank (BB). The BB responds to the changes in a power imbalance between PV generation and demand within an autonomous DC microgrid. The power loss during charging/discharging of the battery is the great challenge for the autonomous DC microgrid supplied by PV. It decreases the system efficiency. The control objective of the proposed DSM scheme is to use the PV energy more efficiently. The proposed control algorithm shifts the deferrable load from non-sunny hours to sunny hours and decreases the building demand during non-sunny hours. In this way it decreases the charging/discharging cycles of the batteries. This is reducing the power losses in the battery and improves system efficiency. The proposed scheme reduces the size of the PV plant, storage and capital cost of the system. The results showing a clear shifting of the load so that to get significant reduction in the system cost which is given numerically as percentatge saving.

Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study

The energy demand in urban areas has increased dramatically over the last few decades because of the intensive urbanization that has taken place. Because of this, the European Union has introduced directives pertaining to the energy performance of buildings and has identified demand side management as a significant tool for the optimization of the energy demand. Demand side management, together with thermal energy storage and renewable energy technologies, have mainly been studied so far at a building scale. In order to study and define potential demand side management strategies at an urban scale, an integrated urban scale assessment needs to be conducted.DiDeProM, a model that can be used to generate detailed thermal energy demand profiles, at an urban district scale, has been developed in the current study. It is a bottom-up engineering model, based on samples of the representative building technique. A parametric analysis of the important variables of building energy performance at an urban scale has then been carried out. This has generated a database of normalized thermal energy demand profiles with an hourly time resolution. The final step of the process includes the generation of a detailed overall thermal energy demand profile at an urban district scale.DiDeProM was applied to a block of buildings in Turin (Italy) as a case study. After the calibration of the simulation model on real monitored data, a parametric analysis on 300 scenarios for a reference building was conducted, generating a database of seasonal thermal heating energy demand profiles with hourly time steps. An average hourly heating profile was generated from this database according to a specific aggregation approach. The DiDeProM application indicated that the model works properly at the scale of a typical small block of buildings, and it is able to generate a total thermal energy demand profile, with detailed time resolution, at an urban district scale. These profiles will be used to create demand side management strategies that will integrate thermal energy storage and renewable energy technologies at a district scale.

A two-stage Energy Management System for smart buildings reducing the impact of demand uncertainty

This paper proposes a novel two-stage Energy Management System (EMS) that is suitable for small-scale grid-connected electrical systems, such as smart homes and buildings, encompassing renewable generators and electrical storage. In such systems, forecast errors of renewable generation and energy demand profiles result in a significant uncertainty on the power exchanged between the end users and the utility grid. The proposed EMS reduces such demand uncertainty and the electricity bill for end users, at the same time. The main novelty of the proposed technique is that it does not require any change in pricing plans or user's habits, differently from classical Demand Side Management schemes. Moreover, thanks to the increased predictability of the exchanged power, utility providers are facilitated in managing the wholesale risk, for example by designing appropriate pricing schemes. The proposed EMS is based on an optimization algorithm. It starts from profiles of renewable generation and load demand, which are obtained by a forecasting method based on suitably chosen and trained Artificial Neural Networks. Furthermore, it has been designed to be suitable for an embedded implementation on low-performance processing platforms. The proposed EMS has been validated using datasets coming from monitoring campaigns. The considered case study is a smart home with an annual energy consumption of about 4500kWh. It encompasses a grid-connected electrical distribution power plant with a 3kW photovoltaic generator and a 4.6 kWh battery electrical storage system. The results obtained for a sample month demonstrate the effectiveness of the approach. As a matter of fact, the demand uncertainty is only 4.75% against a cumulative forecast error of 10.35% expressed as normalized root mean square error. At the same time, the end user's cash flow is 2.43% higher than the income obtained without an EMS.

Design of an Approximate Dynamic Programming based neural controller for Smart Home Energy Management

Demand Side Management (DSM) is the control of consumer demand for energy via different techniques such as financial incentives. This technology has become inevitable in the new smart grid infrastructure. In this study, a DSM scheme, a novel smart home energy management system, is proposed. The goal, defined in terms of cost, is to manage the home energy system according to time-varying prices in a way that energy demand from grid is reduced as much as possible or it is moved to off-peak times. The proposed scheme takes advantage of local energy generation, energy storage unit and schedulable load. Our offline scheme uses an Adaptive Dynamic Programming (ADP) based algorithm to solve the energy management problem and optimally schedule the battery and load operations in a given time horizon. We also use PSO method to solve the mentioned problem. The results obtained by PSO are used as an element of comparison. Simulation results show that the ADP algorithm can reduce costs with respect to PSO due to better decision making ability.

Efficient Computation for Sparse Load Shifting in Demand Side Management

This paper introduces a distributed algorithm for sparse load shifting in demand-side management with a focus on the scheduling problem of residential smart appliances. By the sparse load shifting strategy, customers’ discomfort is reduced. Although there are many game theoretic models for the demand-side management problem, the computational efficiency of finding Nash equilibrium that globally minimizes the total energy consumption cost and the peak-to-average ratio is still an outstanding issue. We develop a bidirectional framework for solving the demand-side management problem in a distributed way to substantially improve the search efficiency. A Newton method is employed to accelerate the centralized coordination of demand side management strategies that superlinearly converge to a better Nash equilibrium minimizing the peak-to-average ratio. Furthermore, dual fast gradient and convex relaxation are applied to tackle the sub-problem for customers’ best response, which is able to relieve customers’ discomfort from load shifting or interrupting. Detailed results from illustrative case studies are presented and discussed, which shows the costs of energy consumption and daily peak demand by our algorithm are reduced. Finally, some conclusions are drawn.

An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data

The present study uses power data from panel meter connected to the micro-grid to identify electrical energy consumption of the school appliances and their behavior in both warm-up, standby and active operation states. Before the conduction of experiments a load auditing of the school appliances was carried out by reading the rated power of each device (e.g. photocopy machine, printer, and fridge). The captures of this kind of information were essential as it provides a starting point to determine energy use of each appliance and hence simplify the process of identification. The electric parameters such as active power, reactive power and current were used to analyze the behavior of electrical appliances in all states. Experimental results show that, both active and reactive power were found to be high for old Canon photocopy machine as compared to new Canon photocopy machine. Another experiment reveals that printing one copy by using HP laser printer consumes about 700 W, while photocopying one copy with new Canon machine utilizes approximately 1100 W. This study concludes that new photocopy machine consumes more electric energy in warm-up state as compared to other states (standby and active operation states). Future work is to develop an algorithm for demand side management strategies which will enable efficient utilization of the electric energy from the micro-grid and hence bring the intended energy impact to the school.