Demand Side Management Methods Research Articles

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy.

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids.

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Demand-Side Management Method for Households with Self-Generation and Storage of Electricity

The main objective is to propose a calculation method for assessing the benefits of individual domestic prosumers in self-consumption and economic savings when managing their own energy resources. The paper applies the demand-side management concept in the residential sector from the individual domestic perspective so that customers can understand the value of their own sustainable energy resources, conducting self-generation and demand management. The novelty lies in allowing the prosumer to manage their own energy resources to their benefit at a reasonable cost, instead of participating in automated large residential demand-side-management programmes that respond to the means of the grid system operator or other energy service companies, such as aggregators. A methodology for calculating the self-consumption rate and the economic benefit for the consumer is proposed, including three different cases: consumer demand is higher than self-generation, and consumer demand is equal to self-generation, and consumer demand is lower than self-generation. The methodology is validated with actual data from a household in Valencia (Spain) during a complete year, obtaining an average reduction in the annual electricity bill of 70% and a demand coverage with the self-renewable system reaching values of 80% throughout the year. The significance of this methodology goes beyond the economic revenue of the individual consumer; it also aims to guide consumers towards efficient practices in the use of their available energy resources and raise awareness on their energy behaviour.

Perspectives of Demand-Side Management in a Smart Metered Environment

Some distribution system operators (DSOs) - among them all Hungarian DSOs - have installed extensive demand-side management (DSM) infrastructure in order to be able to perform peak-clipping and valley-filling of the daily load curves. These systems rely either on the traditional ripple control (RC) or radio ripple control (RRC) technology. In this paper a short overview on the capabilities of these methods will be given, and compared to the DSM possibilities offered by Smart Metering (SM) based systems. In the focus of the paper a new modelling framework will be presented, which makes it possible to simulate the consumer behaviour on an appliance level in different DSM environments. The effects of direct DSM methods (like RC or RRC) and soft DSM (dynamic tariff systems) on the daily load curves will be analyzed in order to exploit the possibilities of SM systems.

Optimal ESS Scheduling considering Demand Response for Electricity Charge Minimization under Time of Use Price

A dependency of the world on electrical energy has been increasing according to development of industry. To meet increasing power demand, existing generators have been expanded or new generators have been installed continually. But currently, the social acceptance for installing a new plant is difficult due to electromagnetic effects on human body, environmental pollutions, and so on. Therefore supply is not, demand side management (DSM) is need. In recently, various methods of DSM have researched for a long time. Among them, this paper suggests method of optimal ESS scheduling considering demand response (DR) for electricity charge minimization under time of use (TOU) price. The DR is considered as price based programs and incentive based programs. In case study, load of a practical industrial customer was managed by the proposed DSM method. The simulation results show the effectiveness of the method.

Assessment of impacts of peak-shaving programs on energy costs in the residential sector

Demand-side management, which includes various methods and mechanisms, plays a significant role in the management of energy systems. This paper examines the impacts of different demand-side management methods on energy costs of a residential load. In this regard, three scenarios have been developed: on-site energy generation, energy storage, and finally, load shifting during peak hours of energy consumption. The three proposed scenarios along with the basic state, were modeled with the help of the System Advisor Model and MATLAB. The modeling results showed that according to the load management process during the peak hours of the year, all three scenarios lead to a reduction in energy consumption costs.

Comprehensive Management of Electricity Demand Distribution in Time

The paper is aimed to strengthen the controllability of electricity consumption mode at all structural layers of the country’s energy system (ES) to establish the optimal load curve in the PS. Following this goal, the energy system was broken down into seven structural layers - from the technological operation to the ES. For each layer, an expert assessment of the effectiveness of six institutional and operational methods of electricity demand-side management (DSM) was done. The integrated application of the suggested methods was tested in two industrial consumers, which proved the effectiveness of this approach for leveling their aggregated load curve. To ensure an appropriate economic impact on the electricity demand, given the influence of individual consumers on the load curve fluctuation in the ES, a particular price function considering the cross-correlation coefficient of the load curves was developed. It was proved that the complex DSM methods application significantly improved the controllability of the electricity consumption mode. To incentivize consumers to adjust their electricity consumption, a special price system functionally related to the cross-correlation coefficient of the consumer and the ES load curves was developed. The marginal price values depending on the cross-correlation coefficient were defined, while the intermediate price values were calculated by the functional transformation of the ES load curve into the price scale. The significance of the research results lies in the fact that ranking the DSM methods by the priority of application for various structural layers of ES and their integrated application almost doubled the DSM effectiveness.

AGGREGATION PRICE MODELS FOR MICROGRIDS WITH DISTRIBUTED ENERGY RESOURCES

The development of Microgrid states the problem of choosing the optimal technologies for its composition.This paper is to present cost based demand-side management methods for the Microgrids with DistributedGeneration sources to optimize their operation. While implementing variable pricing models the aggregatorshould take into account characteristics of different Distributed Energy Resources (DER) (diesel engines, gasturbines, fuel cells, solar panels, small hydropower plants and wind turbines) and LCOE for each type of DER tostimulate each local electricity market participant separately. The system Microgrid using three diesel generatorsand the option of replacing one generator with a solar electric and wind generator installation was considered.

An approach to optimize building area ratios scheme of urban complex in different climatic conditions based on comprehensive energy performance evaluation

In the context of the global energy shortage, urban complexes have attracted more and more attention from energy-saving research due to their large number and great energy-saving potential. While, the energy performance factors are rarely considered in the function scheme decision of urban complex in the recent pre-programming process. Optimizing the building area ratio scheme of urban complex is the key link to improve the comprehensive energy utilization efficiency. This paper proposes an approach to optimize building area ratio scheme of urban complex from energy performance perspective by energy simulation, scenario analysis and demand side management methods, and provides a data set of rankings of urban complex function combination schemes by evaluating the performance of energy load leveling and the degree of load stability of each building area ratio scheme in different climatic conditions, respectively. The results indicate that: 1) the different building area ratios notably affect the integrated operating energy performance of urban complexes with a maximum evaluation indexes difference of more than 85%; 2) the evaluating and optimizing results of the building area ratio schemes show broadly similarity in different climate conditions; 3) the optimal building area ratio scheme of urban complex is usually with less “shopping mall” (0 ∼ 10%), more “residence” (more than 35%), and floating ratio of “office” and “hotel” (0 ∼ 35%). The main findings of this study provide a quantitative basis for the architectural pre-programming and function scheme decision-making of urban complex by demand-side energy management to improve the energy efficiency, so as to guarantee the energy supply security.

Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm

In the era of smart grids and the Internet of Things, demand side management, which aims to reduce electricity bills while increasing user satisfaction by scheduling appliances properly, becomes imperative for residential consumers. As a result of the conflict between the two objectives, it is impossible to optimize them simultaneously. Nevertheless, using multi-objective optimization approaches, trade-off solutions can be obtained. In this paper, a novel demand-side management method is presented to manage the operation of residential appliances. In the beginning, appliances are divided into interruptible, non-interruptible, and power-shiftable types according to their operating characteristics and the user’s preferences. And the mathematical models are built accordingly. Then, a multi-objective optimization problem is formulated to minimize the electricity cost and user dissatisfaction, in which residents’ tolerance to discomfort is considered. Since it is a multi-objective mixed integer nonlinear programming problem, a hybrid meta-heuristic algorithm is proposed to solve it efficiently. The experiment results have confirmed the effectiveness of the optimization model and the higher efficiency of the hybrid algorithm. Furthermore, a case study has been performed to demonstrate the effectiveness of the scheduling method.

Peak power management of residential building using demand side management strategies

Peak power management is one of the demand-side management methods aiming at regulating energy usage throughout the day. This paper discusses peak load, which relates to a consumer's peak demand during specific hourly hours and how to manage it. Shifting loads from demand hours to non peak hours relieves pressure on utilities to meet demand and supply while also lowering the cost to consumers. The Peak Load Management Model offers a more effective framework for lowering peak loads and moving loads from peak to off-peak hours. A cascaded artificial neural network is utilised to construct a demand side management strategy for managing peak electricity in residential buildings in this paper. The peak load control model's results and discussion are highlighted in the simulation results and performance review.

Strategy for demand side management effectiveness assessment via a stochastic risk‐based bidding approach in a multi‐energy microgrid containing combined cooling, heat and power and photovoltaic units

This paper proposes an efficient risk-based infrastructure management approach for a multi-energy microgrid to assess the effectiveness of demand-side management (DSM) through a stochastic strategy. The studied microgrid consists of photovoltaic and combined cooling, heat and power as the energy generation units and load aggregator with the aim of DSM. Based on this structure, day-ahead operational scheduling is investigated for the microgrid in different scenarios in both summer and winter seasons. The risk consideration is also performed in the bidding procedure dealing with different scenarios and variable prices and probabilities. In this study, the conditional value at risk index is considered to assess the amount of risk and is also studied from two perspectives. In the first case, the level of risk acceptable to the investor, the load aggregator, is selected, and its impact on the day-ahead scheduling is examined. In the second case, the effect of different levels of risk-taking and their importance on the scheduling procedure are examined. The main purpose is to show the shortcomings of the traditional DSM method when load management is done for private companies with a certain level of risk and prepare the ground for future studies to solve them.

The role of demand response in residential electricity load reduction using appliance shifting techniques

PurposeSome of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa.Design/methodology/approachThe research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior.FindingsThis study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions.Research limitations/implicationsIt should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households.Practical/implicationsLoad shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers.Originality/valueThis paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea

In today’s power systems, the widespread adoption of smart grid applications requires sophisticated control of load variability for effective demand-side management (DSM). Conventional Energy Storage System (ESS)-based DSM methods in South Korea are limited to real-time variability control owing to difficulties with model development using customers’ load profiles from sampling with higher temporal resolution. Herein, this study thus proposes a method of controlling the variability of customers’ load profiles for real-time DSM using customer-installed ESSs. To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. The proposed ESS-based DSM strategy primarily reserves capacity in ESSs based on estimated variation in load, and performs real-time maximum demand control with the reserved capacity during scheduled peak shaving operations. To validate the proposed methods, this study used load profiles accumulated from industrial and general (i.e., commercial) customers under the time-of-use (TOU) rate. Simulation verified the improved performance of the proposed ESS-based DSM method for all customers, and results of Kolmogorov-Smirnov (K–S) testing indicate advances in the proposed hybrid estimation beyond the stand-alone estimation using the MTM- or ANN-based approach.

Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

Demand Side Management based techno-economic performance analysis for a stand-alone hybrid renewable energy system of India

ABSTRACT Today, the off-grid Integrated Hybrid Renewable Energy System (HRES) is considered to be an ambitious source of electrical power due to its technological, economic, and environmental benefits. Optimal integration of renewable energy resources has been found the most viable option for powering of stand-alone remote rural area in Karnataka. This study presents Demand Side Management (DSM)-based techno-economic analysis under Load Following (LF), Cycle Charging (CC), and Combined Dispatch (CD) strategies with Lead Acid (LA) and Lithium-Ion (Li-Ion) batteries. Strategic conservation and peak shifting DSM methods are considered in the present work for evaluating the optimal hybrid system using Hybrid Optimization of Multiple Energy Resources (HOMER®). The optimization results of the LA, Li-Ion batteries-based HRES are compared with and without DSM using two different configurations. From the results, it is observed that the Li-Ion battery-based photovoltaic (PV)/Micro Hydro Power (MHP)/battery (BT) HRES under CD strategy gives the optimal solution. The corresponding net present cost (NPC) and cost of energy (COE) are found to be as $3,14,079 and 0.103 $/kWh, respectively. The implementation of DSM saves 33% in NPC and 0.003 $/kWh in COE, respectively. Further, a sensitivity analysis has also been carried out to evaluate the effect of input parameters such as fuel cost, PV cost, battery cost, and load demand on NPC, COE, and renewable fraction (RF).

Peak shaving potential analysis of distributed load virtual power plants

As an important demand-side management method, distributed load participation in demand response provides an important means for auxiliary services such as peak-clipping and calming fluctuations of renewable power generation. Air conditioning load is usually viewed as a typical thermostatically related load, and is widely distributed in residential areas. Residential air conditioning load is a kind of demand response resource with broad application prospect. During the peak load period in summer, air conditioning load accounts for more than 40% of urban electricity consumption. Moreover, air conditioning load has certain flexibility and controllability. Reasonable evaluation of its peak clipping potential can effectively promote the safety and economic efficiency of the power grid. This paper proposes an analysis method of air conditioning load demand response potential combined with electricity consumption data and physical model. Subsequently, the switching machine and working state of air-conditioning load are identified, model parameters are calculated, and the dynamic adjustment potential of air-conditioning load in the future period is analyzed. In this paper, the data of electricity consumption in a province of China is used in the case study, and the algorithm is verified by examples.

Estimating synthetic load profile based on student behavior using fuzzyinference system for demand side management application

This paper proposes a novel approach of estimating synthetic load profiles based on the electrical usage behavior using the fuzzy inference system (FIS) for demand side management (DSM). In practice, DSM is utilized to change the pattern of electrical energy consumed by end-users to modify the load profile by manipulating the price of the electricity. This study focuses on the energy consumption consumed by students who are paying electricity bills indirectly. Therefore, the effectiveness of conventional DSM methods on this user requires further investigation. In this study, the FIS estimates the synthetic load profile based on the student?s behavior profile. Then, three DSM techniques: load clipping, load shifting, and load conservation, are applied to the electrical usage behavior model. The FIS estimates the synthetic load profile based on the modified electrical usage behavior model with these DSM techniques. From this estimation, the synthetic load profiles are analyzed and compared to evaluate the effectiveness of the DSM methods on the students. The result shows that the FIS estimates the synthetic load profile satisfactorily. Also, load conservation is the most effective technique in reducing the peak load profile and power consumption for this type of user. Conclusively, the result implies that the proposed methodology can be used to evaluate the effectiveness of the DSM method to reshape the load profile.

A Coordinated Performance of Power System Operated with Participants in Demand Response Programs Considering Environmental Pollution Constraints

In a re-structured environment, demand response (DR) programs are presented in a new style. The demand response program consists of demand-side management methods which answers to electricity price variations. With advent of electricity markets, the demand-side management programs was introduced into two categories: (1) energy efficiency program, (2) demand response program. This paper studies engagement of participants load in an electricity market having a grid with large and small loads interested to participate in the market, energy storage systems, micro-grids, and distributed generations. In the proposed scheme, it is supposed to have an aggregator which sends demand-side preferences including load curtailment, load shifting, onsite generation, and energy storage systems along with proposed value and prices and all respective system constraints to the independent system operator (ISO). In fact, load aggregators submit aggregated DR offers to the ISO in order to optimize final decisions on aggregators’ DR contributions in wholesale market. The main goal of this paper is to solve the operation problem considering demand-side, system, and pollution and reliability constraints. Compared to other methods, the results indicate that the explicit modeling of customer DR would provide ISOs with more flexible options for scheduling the available energy resources in day-ahead energy markets.

Optimal Sizing of Shipboard Carbon Capture System for Maritime Greenhouse Emission Control

Under increasingly stringent emission regulations, carbon capture system (CCS) is a feasible alternative to reduce the shipping greenhouse gas (GHG) emission before the maturity of renewable energy technology. In this sense, this article proposes an optimal sizing method to determine the capacity of shipboard CCS under strict energy efficiency operating index (EEOI) constraint. The proposed model is formulated as a two-stage planning problem, where the first stage is to determine the capacity of CCS and the expanded capacity of energy storage system to sustain the operation of CCS, and the second stage is a joint shipboard generation and demand-side management model to address the power shortage issue led by the CCS integration. Extensive simulations demonstrate that under EEOI constraint, the CCS integration is feasible to reduce the shipping GHG emission, and the proposed joint generation and demand-side management method is able to relieve the power shortage issue of shipboard CCS. The corresponding average carbon capture level increases 11.9% with the joint management.