Demand Side Management Strategies Research Articles

A benefit cost analysis of strategic and operational management options for water management in hyper-arid southern Peru

This paper presents findings from a stochastic benefit-cost analysis (BCA) using Monte Carlo simulations to estimate the probable values for net benefit and benefit cost ratio for a series of proposed strategic and operational water management options under climate change and socioeconomic development. The study is performed in the Tacna region in southern Peru, geographically within the Atacama Desert and under prevailing water scarcity conditions. We examined the benefits and costs to four main water users: urban, agriculture, mining and the environment over a 30-year time horizon. The main scenarios under consideration include reduced water demand from improved water use efficiency in the urban and agriculture sector, and investment in water supply infrastructure including inter-basin infrastructure and dams, a desalination plant, and increased groundwater extraction. Our findings suggest that implementation of demand-side management strategies, which in this instance is investment in water use efficiency technology and water conservation measures result in more favourable net benefit outcomes than investment in water supply infrastructures. The primary outcome of this study is to help the Regional Government of Tacna, Peruvian water authorities and water users, make more informed decisions about water investment options going forward, thereby supporting regional development opportunities.

A Critical Review on the Impacts of Energy Storage Systems and Demand-Side Management Strategies in the Economic Operation of Renewable-Based Distribution Network

Energy storage systems (ESSs) and demand-side management (DSM) strategies have significant potential in providing flexibility for renewable-based distribution networks. Therefore, combining ESSs and DSM strategies with renewable energy sources (RESs) to solve economic, operational, environmental, and power-related political issues has received special attention from power system planners around the world. In this regard, developed countries, which are pioneers in renewable technologies, have proposed various supportive policies and practices for the widespread use of ESSs and DSM strategies in the context of distribution networks. Hence, this paper performs a comprehensive review of the most recent actions taken to build the infrastructure necessary to achieve 100% renewable energy. On this basis, this paper firstly surveys the necessity of using ESSs and DSM strategies in renewable-based distribution networks. Then, the existing policies and incentive programs implemented in different countries for the development of RESs in optimal coordination with ESSs and DSM strategies are presented. The impacts of utilizing ESSs and DSM strategies in improving the economic performance of the renewable-based distribution networks are also investigated. Finally, prevalent energy management strategies, which are proposed to optimize utilization of ESSs and DSM strategies in renewable-based distribution networks, are investigated from the perspective of optimization approaches.

Multi-quantile recurrent neural network for feeder-level probabilistic energy disaggregation considering roof-top solar energy

The purpose of feeder-level energy disaggregation is to decouple the net load measured at the feeder-head into various components. This technology is vital for power system utilities since increased visibility of controllable loads enables the realization of demand-side management strategies. However, energy disaggregation at the feeder level is difficult to realize since the high penetration of embedded generation masks the actual demand and different loads are highly aggregated. In this paper, the solar energy at the grid supply point is separated from the net load at first via either an unsupervised upscaling method or the supervised gradient boosting regression tree (GBRT) method. To deal with the uncertainty of the load components, the probabilistic energy disaggregation models based on multi-quantile recurrent neural network model (multi-quantile long short-term memory (MQ-LSTM) model and multi-quantile gated recurrent unit (MQ-GRU) model) are proposed to disaggregate the demand load into thermostatically controlled loads (TCLs), non-thermostatically controlled loads (non-TCLs), and non-controllable loads. A variety of relevant information, including feeder measurements, meteorological measurements, calendar information, is adopted as the input features of the model. Instead of providing point prediction, the probabilistic model estimates the conditional quantiles and provides prediction intervals. A comprehensive case study is implemented to compare the proposed model with other state-of-the-art models (multi-quantile convolutional neural network (MQ-CNN), quantile gradient boosting regression tree (Q-GBRT), Quantile Light gradient boosting machine (Q-LGB)) from training time, reliability, sharpness, and overall performance aspects. The result shows that the MQ-LSTM can estimate reliable and sharp Prediction Intervals for target load components. And it shows the best performance among all algorithms with the shortest training time. Finally, a transfer learning algorithm is proposed to overcome the difficulty to obtain enough training data, and the model is pre-trained via synthetic data generated from a public database and then tested on the local dataset. The result confirms that the proposed energy disaggregation model is transferable and can be applied to other feeders easily.

Study on load monitoring and demand side management strategy based on Elman neural network optimized by sparrow search algorithm

PurposeThis paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.Design/methodology/approachThe paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.FindingsThe paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.Research limitations/implicationsBecause of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.Practical implicationsThe paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.Originality/valueThis paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.

A control-oriented scalable model for demand side management in district heating aggregated communities

District heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. Additional benefits can derive from novel demand side management strategies, which can efficiently balance demand and supply. However, their implementation requires detailed knowledge of heating network characteristics, which vary remarkably depending on urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. For this purpose, the present work proposes a novel procedure to develop a fast scale-free model of large-scale district heating networks for system optimization and control. Each network community is represented and modeled as an aggregated region. Its physics-based model is identified starting from a limited amount of data available at the main substations and includes heat capacity and heat loss coefficients. The procedure is demonstrated and validated on the network of Västerås, Sweden, showing results that are in agreement with data from the literature. Thus, the model is well suited for real-time optimization and predictive control. In particular, the possibility to easily estimate the heat storage potential of network communities allows demand side management solutions to be applied in several conditions.

Development of a Day-Ahead Demand Side Management Strategy to Improve the Microgrid Efficiency

Currently, commercial and industrial microgrids comprising wind and solar farms operating with conventional plants are being established globally. Management of such microgrids requires bearing in mind a side effect of green generation, which manifests itself in the difficulty of predicting this generation. Prediction errors introduce uncertainties in the state variables, which results in active power imbalances. These imbalances can be eliminated using power reserves. The active consumer resources, handled by the demand-side management system according to its strategy, can be considered such reserves. The paper proposes an algorithm of determining in advance the demand-side management strategy based on which one can develop the recommendations about the size of control actions and the time of their execution for each active consumer. An example of building a strategy for a demand-side management system installed in a microgrid that consists of a wind farm, a battery energy storage system, two conventional plants, and two active consumers is given.

Demand-side Management Based on Model Predictive Control in Distribution Network for Smoothing Distributed Photovoltaic Power Fluctuations

With the rapid increase of distributed photovoltaic (PV) power integrating into the distribution network (DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demand-side needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling (PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment. Thus, a demand-side management strategy based on model predictive control (MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally, the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.

Modeling of Demand Side Management Scheme for Matching Electricity Supply with Demand in the Nigerian Power System

Modeling of Demand Side Management Scheme for Matching Electricity Supply with Demand in the Nigerian Power System

A Novel Residential Energy Management System Based on Sequential Whale Optimization Algorithm and Fuzzy Logic

Demand side management has become inevitable in today’s smart gridenvironment to balance electricity supply and demand. Many methodolo-gies/algorithms have been developed for realizing and implementing thistechnique at different levels of distribution systems. Advanced meteringinfrastructure and the latest communication technologies have empow-ered residential consumers to participate in the demand side managementschemes. After careful investigations and analyses, the authors of this paperhave made a decisive effort to propose a novel sequential strategy for devel-oping an energy management system for scheduling loads of residentialconsumers. The proposed work aims at a fuzzy logic and an evolutionaryalgorithm-based approach of demand side management that considers theusers’ preference of operating time of the appliances at the residence of theirchoice, which has not been addressed earlier. This approach reduces the peakdemand and cuts the cost of electricity per billing period for a consumer. This study also encourages the consumers to install solar rooftop PV systemsby indicating the cost benefits reaped over a more extended period. Theproposed framework is implemented in MATLAB, and the case studies provethe effectiveness of using this algorithm from the consumers’ perspective

A Novel Demand Side Management Scheme for Residential Buildings Using Smart System

A Novel Demand Side Management Scheme for Residential Buildings Using Smart System

Day‐ahead electricity pricing mechanism considering the conflict between distribution network congestion and renewable produce

There is practically no published work that considers distribution network use of system (DUoS) charges as a price signal in the day ahead-market, thus failing to establish a trade-off between the DUoS charges and the energy procurement costs. This paper presents a new approach to optimal design of a day-ahead pricing mechanism for managing distribution network congestion caused by renewable generation. The proposed concept of day-ahead pricing is designed to minimize customer electricity bills taking account of line power constraints resulting from increased penetration of renewable generation. It is assumed that each user is equipped with controllable appliances and a smart meter enabling two-way communications between suppliers and their customers. Suppliers employ an hourly demand side management strategy which considers the price signal from distribution network operators (DNOs), distributed generations (DGs) and wholesale market. A weighted average DUoS charge approach is applied in this paper, allowing DNOs to send a cost reflective use of system charge to electricity users in different locations based on their hourly predicted day-ahead power consumption. An optimum day-ahead pricing mechanism has been developed for DGs to manage their renewable resources to achieve revenue reconciliation and maximize generation output. Finally, power suppliers can optimally manage the controllable appliances that installed in each end user and balance the energy consumption from the main grid and DGs to minimize the electricity price. Several case studies have been conducted with a modified IEEE-33 bus distribution network, where a large-scale nonlinear optimization programming algorithm is applied to solve the problem.

Global Strategies to Minimize Environmental Impacts of Ruminant Production.

Demand for ruminant products (dairy products, beef, and sheep meat) is increasing rapidly with population and income growth and the acceleration of urbanization. However, ruminant animals exert the highest environmental impacts and consume the most resources in the livestock system. Increasing studies have focused on various measures to reduce ammonia, greenhouse gas emissions, and resource depletion from ruminant production to consumption. This review offers supply- and demand-side management strategies to reduce the environmental impact of ruminant products and emphasizes the mitigation potential of coupling livestock production with cultivation and renewable energy. On a global scale, more attention should be paid to the green-source trade and to strengthening global technology sharing. The success of these strategies depends on the cost effectiveness of technology, public policy, and financial support. Future studies and practice should focus on global database development for sharing mitigation strategies, thus facilitating technology innovations and socioeconomic feasibility.

Integrated Transportation, Building, and Electricity System Models to Explore Decarbonization Pathways in Regina, Saskatchewan

In Canada, the majority of urban energy demand services the transportation or building sectors, primarily with non-renewable energy sources including gasoline and natural gas. As a result, these two sectors account for 70% of urban greenhouse gas (GHG) emissions. The objective of this paper is to explore the potential for co-benefits when simultaneously electrifying transportation and building demand sectors while expanding variable renewable energy (VRE) production. The investigation uses a novel integrated framework of the transportation, building, and electricity sectors to represent the operational implications of demand side flexibility on both the demand and supply side of the energy system. This original approach allows for very fine temporal and spatial resolution within models, while still performing a multi-sector analysis. First, the activity-based transportation model produces passenger travel demand profiles, allowing for investigation of potential electricity demand and demand response from electric vehicles with high spatial and temporal resolution. Second, the archetype-based building model predicts electricity demand of the residential building sector, allowing for investigation into demand-side management strategies such as load-shifting, building retrofits, and changes in appliance technology. Third, the electricity system production cost dispatch model is used to model the operations of Regina's electricity grid and has a spatial resolution capable of assessing individual and connected positive energy districts as well as VRE integration. Through linking of these three models, the effects of consumer flexibility in transportation and building energy demand are explored, especially in the context of introducing much needed flexibility for large-scale VRE integration. A utility-controlled demand response (DR) strategy is explored as means for Regina to reach their renewable target, along with battery storage. Various pathways to Regina's target are considered, based on the various proposed scopes of the target. The results show that Regina can meet their renewable target with large-scale rooftop solar and wind capacity. DR strategies are marginally effective in aiding toward the renewable target, but, when implemented in conjunction with battery storage, is able to get Regina to within 1% of their renewable target.

Demand Side Management Techniques for Home Energy Management Systems for Smart Cities

In this paper, three distinct distributed energy resources (DERs) modules have been built based on demand side management (DSM), and their use in power management of dwelling in future smart cities has been investigated. The investigated modules for DERs system are: incorporation of load shedding, reduction of grid penetration with renewable energy systems (RES), and implementation of home energy management systems (HEMS). The suggested approaches offer new potential for improving demand side efficiency and helping to minimize energy demand during peak hours. The main aim of this work was to investigate and explore how a specific DSM strategy for DER may assist in reducing energy usage while increasing efficiency by utilizing new developing technology. The Electrical Power System Analysis (ETAP) software was used to model and assess the integration of distributed generation, such as RES, in order to use local power storage. An energy management system has been used to evaluate a PV system with an individual household load, which proved beneficial when evaluating its potential to generate about 20–25% of the total domestic load. In this study, we have investigated how smart home appliances’ energy consumption may be minimized and explained why a management system is required to optimally utilize a PV system. Furthermore, the effect of integration of wind turbines to power networks to reduce the load on the main power grid has also been studied. The study revealed that smart grids improve energy efficiency, security, and management whilst creating environmental awareness for consumers with regards to power usage.

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.

A Demand Side Management Approach For Optimal Sizing of Standalone Renewable-Battery Systems

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission.

Optimal day ahead energy consumption management in grid‐connected microgrids

The day-ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on input-output characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day-ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load-shaping based demand-side management strategy is adopted to reduce the peak loads and enhance the DG's unit operational costs. The impact of demand-side management and price-driven demand response programs on convex and nonconvex energy management system (EMS) problems is investigated. Furthermore, the short-term scheduling horizon of 15-minutes resolution time is considered for both solar and wind power to maintain forecast accuracy. The state-of-art optimization algorithm of quantum particle swarm optimization is devised to solve the proposed problem in the presence of the nonconvex cost function of DGs. The technical performance indices for each demand response program is evaluated, and the best alternative demand response program is chosen by implementing analytical hierarchy process. The proposed algorithm efficiently solves the nonconvex EMS problem, and the simulation results yield a 12.11% reduction in operating cost without compromising customer satisfaction. Computational time, convergence characteristics, and solution effectiveness in contrast to recently reported metaheuristic algorithms are examined for the effectiveness of the suggested algorithm.

Design and control of radiant ceiling panels incorporating phase change materials for cooling applications

Experimental and numerical studies have demonstrated that thermally activated building systems (TABS) may lead to significant energy savings. However, TABS are generally incorporated into the building during the construction phase, limiting their adoption to new buildings. To encourage the application of TABS during building refurbishments, the authors have developed a radiant ceiling panel (RCP) with macroencapsulated phase change materials (PCM). This study aims to provide the criteria to design, size, and control the newly proposed RCP-PCM system. A simplified method to size and design the RCP-PCM system for cooling applications is developed from a set of parametric dynamic simulations. At first, the thermal storage properties of the macro-encapsulated PCM were determined using the standard ASTM C1784-20. The obtained properties were then used in a whole-building simulation model validated using measurements in a small test chamber that replicates the conditions of an actual test room. The PCM panel thickness of 0.015 m and a supply water temperature of 15 ℃ showed the best results in terms of thermal comfort and effective thermal energy storage capacity. The implementation of the simplified method in a case study showed that the RCP-PCM system maintained room conditions within the specified thermal comfort range (−0.5 < PMV < 0.5) for more than 90% of the occupied periods in all of the evaluated cooling-dominated climates. Moreover, yearly-round, the PMV values never reached values higher than 0.8 or lower than −0.6, confirming the effectiveness of the proposed method for designing a RCP-PCM system. The results show that energy savings of 22% could be obtained in a very hot and humid climate using an RCP-PCM system instead of a conventional all-air system. In conclusions, this paper offers a new system to promote energy flexibility and Demand-Side Management (DSM) strategies to modulate the energy demands in retrofitted buildings.

Energy flexibility curves to characterize the residential space cooling sector: The role of cooling technology and emission system

Space cooling of buildings shows an increasing trend in energy use worldwide. The exploitation of the energy flexibility reserve obtainable from buildings cooling-loads management can have an important role to improve the security and the reliability of the electricity power grid. Many studies in literature assess the energy flexibility potential of air conditioning systems; however, the role of the specific cooling technology is always scarcely explored. The objective of this work is to provide an evaluation of the operational energy flexibility that can be obtained involving the most common residential space cooling technologies, paying particular attention to the distribution system (e.g., all-air system, fan-coil units with and without the addition of a thermal energy storage and hydronic massive systems). The analysis is carried out with dynamic simulation models for the various cooling systems involved. Results show a great influence of the adopted distribution system in the implementation of a flexibility request. In particular, all-air systems (i.e. split systems) show the lower flexible behavior (they require up to 10 h of precooling to be off during a peak hour). Whereas the adoption of fan coil units coupled with a thermal energy storage allows to implement different peak shaving strategies without compromising the indoor air temperature with low drawback effects in terms of anticipated electricity overconsumptions (no precooling of the air is required and a maximum of 23 % increase in electricity consumed in the time before the event occurs, with a reduction of 16 % in subsequent hours). In case of ceiling cooling systems, results highlight that as the thermal inertia of the system increases, the indoor conditions are less affected, but the anticipated overconsumption of the heat pump increases (for the same Demand Response event the electricity overconsumption goes from + 67 % to +116 %, passing from ceiling panels to concrete ceiling). The results obtained from this analysis are then used to draw flexibility curves, which aim at providing a characterization of the flexibility of a cooling system. They can be used to predict, for typical installations, the system behavior in presence of a peak power reduction strategy in terms of pre-cooling duration, energy use variation and modification of the temperature comfort bandwidth. Such predictions are important because they can provide insights on the design and operation of space cooling systems in demand side management strategies.

Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids

With the rapid proliferation of non-dispatchable energy sources, the need for demand-side management (DSM) strategies has become crucial to ensure affordability and reliability for end-users. The application of various diversified DSM strategies in the microgrid energy management system (EMS) is gaining popularity. This paper aims to solve the energy management problem of the microgrid in conjunction with both customer-oriented and utility-oriented DSM strategies for the first time in the literature. In light of this, a stochastic EMS framework is developed to implement and analyze the flexible load shaping DSM strategy, price-based, and incentive-based demand response programs (DRPs) in the presence of non-dispatchable energy resources. Further, the flexible price-oriented load model is adopted for price-driven and incentive-driven DRPs to depict the realistic assessment of consumers’ sensitivity to market prices. The scenario construction approach is employed to address the stochastic nature of renewable power generation, market prices, and load demand. With the complexities as mentioned above, the problem needs to be solved with a powerful optimizer sufficiently to enhance energy efficiency and optimize energy utilization. Hence, the recently reported novel metaheuristic algorithm (Black Widow Optimization) is applied to solve the proposed MG energy management problem in the MATLAB environment. The IEEE-34 node distribution feeder-based MG network is modified to study the proposed algorithm's performance, and a detailed analysis of various techno-economic indices is presented. The obtained simulation results are compared with existing popular algorithms to prove the efficacy of the proposed algorithm in terms of convergence, computational time, an optimum solution and the real-time market bid prices were considered in analysis for day-ahead scheduling of microgrid network.