Implementation Of Demand Side Management Research Articles

The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64).

Optimization of design and operation of a digestate treatment cascade for demand side management implementation

Sustainable chemical engineering through demand side management (DSM) and renewable feedstock integration e.g. in biorefineries are key to optimizing the use of fluctuating energy resources and minimizing environmental impact while conserving resources. This contribution presents the results of the economic evaluation of integrating DSM into biofuel biorefineries through a dynamic simulation approach. A previously developed decision support tool for DSM implementation was extended to describe the size of intermediate buffer tanks as a function of oversizing up- and downstream processes. Design optimization of the process cascade determined the oversizing that allows the optimal balance of operational cost reduction through flexibility and capital cost increase through oversizing. Scheduling optimization validated the results of the steady-state optimization and show that, by considering interactions between processes, buffer tank capacity can be reduced, while increasing DSM potential.

Maximizing Building Energy Efficiency Through Zone-Based Conditions: An Advanced Dynamic Simulation Approach

Abstract The primary objective of this research is to maximize the building energy efficiency by identifying the zone-based conditions and employing an advanced energy simulation model. It is possible to achieve this by reducing periods of excessive heating and cooling by utilizing a calibrated energy model with 15-minute measured data. Further, by regulating the energy consumption of various functional zones corresponding to the work schedules, building's energy system timetables, and the sensible temperature comfort requirements, additional energy savings can be achieved. To show the effect of dynamic simulation on building energy consumption, periodic 15-min temperature data were collected in different zones to be used both in the calibration of the energy model and in the improvement of the current energy profile. Mean bias error and cumulative variation of the root mean squared error were chosen as a performance indicator. Utilizing measured data, over-heated and over-cooled periods were defined by manually identifying zone-based indoor comfort conditions to predict improvements in overall building energy performance. The potential energy savings that can be achieved by largely eliminating over-heating periods are calculated. In addition, energy needs of different zones were considered and zone-based scheduling and zone-based comfort conditions were applied with the implementation of demand-side management. As a result, 17% energy efficiency can be achieved with an automated heating system that controls the indoor temperatures and ensures that the temperature is always kept at the desired level. As a further improvement, 32% energy efficiency can be achieved by applying zone-based scheduling and comfort conditions.

Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management

This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system. The DSM program not only enhances power grid security but also lowers operational costs. The research addresses the DED problem with and without DSM implementation to analyze its impact. Demonstrating effectiveness on two test systems, the suggested method's efficacy is showcased. The recommended method's simulation results have been compared to those obtained using Cheetah Optimizer Algorithm (COA) and Grey Wolf Optimizer. The optimization results indicate that, for both the 10-unit and 20-unit systems, the proposed ECOA algorithm achieves savings of 0.24% and 0.43%, respectively, in operation costs when Dynamic Economic Dispatch is conducted with Demand-Side Management (DSM). This underscores the advantageous capability of DSM in minimizing costs and enhancing the economic efficiency of the power systems. Our ECOA has greater adaptability and reliability, making it a promising solution for addressing multi-objective energy management difficulties within microgrids, particularly when demand response mechanisms are incorporated. Furthermore, the suggested ECOA has the ability to elucidate the multi-objective dynamic optimal power flow problem in IEEE standard test systems, particularly when electric vehicles and renewable energy sources are integrated.

Improving Grid Reliability in Jordan: A Proposal for Integrated Demand Side Management and Energy Storage Solutions

<p align="left">Increased use of Renewable energy systems (RES) in Jordan has posed a major challenge in maintaining our grid stability. Jordan a country that relies on imported natural resources to power up its grid is using more and more RES with an aim to cut back on costs and CO<sub>2</sub> emissions. This paper investigates the usage of Demand Side Management (DSM) and Energy Storage Systems (ESS) to improve the grid’s reliability. A survey was conducted to analyze the opinion and acceptance of the Jordanian population on the implementation of DSM in Jordan. (ESS) mentioned in detail in this paper are: Compressed Air Energy Storage Systems (CAES), Superconducting Magnetic Energy Storage Systems(SMES), and Pumped Hydro Energy Storage Systems(PHES). The survey conducted showed a general scope of willingness of participation from people with 71.7% out of the 385 individuals who participated in the survey saying that they will participate in such a program if implemented in Jordan.</p>

Real-time validation of RoCoF – Enhancing advanced coordinated control strategy for voltage unbalance mitigation implementing demand side management

The absence of rotational masses in converter-interfaced microgrids has led to a reduction in system inertia. As a consequence, the stability of modern microgrids is at risk, especially in the case of unbalanced systems, resulting in significant issues such as an increase in the rate of change of frequency (RoCoF), disturbances in the nominal frequency, and unfavourable voltage unbalances. This research article introduces an advanced concept centered around a virtual synchronous generator designed to enhance RoCoF performance. This is achieved through the implementation of a positive and a negative (PN) - sequence controller scheme in coordination with demand side management to ensure that RoCoF remains within the limit of 2 Hz/s according to IEEE Standard 1547–2018 and the voltage unbalance factor (VUF) adheres to the established standard of 2 % according to IEC 61,000–3–13. The behaviour of the proposed control strategy during transient conditions has also been demonstrated. The framework of the virtual synchronous generator is utilized in this paper to demonstrate the influence of damping coefficient and moment of inertia on the low inertia system's frequency and transient response where it has been established that greater inertia and damping are essential for their enhancement. The study also conducts a small signal stability analysis to assess the effects of various parameters on system stability. Furthermore, the real-time efficacy of the coordinated control methodology based on virtual inertia has been experimentally verified using OPAL-RT simulator. The simulation and experimental results, both provide evidence of the successful performance of the proposed approach in realizing efficient RoCoF enhancement and VUF mitigation.

Optimal demand management of smart energy hybrid system based on multi-objective optimization problem

This paper presents a multi-objective problem for the energy planning of an energy hybrid system. The problem considers three main objectives: minimizing emission pollution and operation cost on the generation side, addressing consumers’ dissatisfaction with the electrical demand, and reducing deviation from optimal levels in the 24 hours ahead to flatten demand profiles. To achieve this, a demand flexibility strategy is implemented, involving the optimal shifting of electrical demand using deferrable loads. The proposed approach utilizes the augmented epsilon-constraint method to identify Pareto solutions for the objectives. Additionally, the TOPSIS decision-making technique is employed to select the optimal solution from the set of Pareto solutions. The effectiveness and robustness of the proposed approach are validated through two case studies. Overall, the paper highlights the importance of considering multiple objectives in the energy scheduling of hybrid systems and demonstrates the effectiveness of the proposed approach in achieving a balance between environmental, economic, and consumer satisfaction goals. The use of demand flexibility strategies and multi-objective optimization techniques can significantly improve the operation of energy systems, paving the way for more efficient energy management practices. The implementation of demand-side management has resulted in a significant reduction of 2.8% and 64.9% in the first and second objectives, respectively, compared to the absence of such management.

Modeling techno-economic multi-objectives of smart homes considering energy optimization and demand management

The research suggests an approach that prioritizes customer needs and aims to reduce energy expenses while safeguarding customer privacy. Furthermore, it is recommended that smart homes incorporate a home energy management system to optimize appliance energy consumption. Conversely, the introduction of demand-side management addresses the energy management challenges faced by smart households. The main goal of this approach is to decrease energy usage and electricity costs for customers. Moreover, it enhances user satisfaction while waiting at common intervals. The primary emphasis of this study is on a smart residence furnished with energy management technology and smart home gadgets capable of supplying electricity to the grid. These objectives are considered distinct aspects in the multi-objective optimization issue stemming from this approach. The study utilizes the grasshopper optimization algorithm (GOA) to optimize battery and home appliance scheduling in smart homes with flexible devices. The goal is to reduce the overall cost of microgrid systems through demand-side management implementation. This comparison highlights the superiority of the proposed method in optimizing energy consumption and reducing carbon emissions in a variety of scenarios. By achieving lower energy costs and carbon emissions while maintaining a comfortable indoor environment, the proposed method proves to be a highly effective and sustainable solution for energy management in buildings. These simulation results provide strong evidence of the method’s potential to significantly impact energy efficiency and environmental sustainability in real-world applications. Furthermore, the consistent minimization of the discomfort index showcases the method’s ability to prioritize occupant comfort while still achieving significant energy savings and emissions reductions. Overall, the comparison with other algorithms solidifies the effectiveness and practicality of the proposed method in addressing the complex challenges of energy management and sustainability in smart homes.

Decision support tool to determine the suitability of demand side management implementation in continuously operated processes – A biorefinery case study

AbstractThe time‐dependent adjustment of a system's power demand simultaneously with current power generation is commonly referred to as demand side management (DSM). DSM strategies are based on the flexibility to purchase electricity at times when prices are low, which can result in monetary benefits. One option to increase the flexibility of continuously operated processes is to oversize them. From an economic point of view, this leads to an increased investment. DSM only serves an economic purpose if the monetary benefits exceed this increase in capital costs. The main goal of this contribution is to develop a decision support tool to help evaluate unit operations regarding their feasibility for DSM implementation. In a case study, the decision support tool was applied to show its functionality on a biomethane production plant. The results show that with the help of the decision support tool, evaluating unit operations concerning their economic DSM potential is possible.

Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations

Demand Side Management (DSM) is a cost-effective approach to managing electricity networks, aimed at reducing capacity requirements and costs, increasing the penetration of renewable generation, and reducing power system emissions. This review article explores the distinctive characteristics of electricity demand in the industrial, commercial, and residential sectors, and their relationship to successful implementation of DSM. The constraints and considerations for DSM are characterized as technical, economic, and behavioral factors, such as process requirements, business operation constraints, and consumer decisions, respectively. By considering all three types of factors and their impacts in each sector, this review contributes novel insights that can inform the future implementation of DSM. DSM in the industrial and commercial sectors is found to be primarily constrained by technical considerations, while DSM in the commercial sector is also subject to economic constraints. Conversely, residential demand is found to be primarily constrained by human behavior and outcomes, highly variable, and the largest contributor to peak demand. This review identifies sector-specific opportunities to enhance DSM uptake. Industrial DSM uptake will benefit from technological and process improvements; commercial DSM uptake can benefit from enhanced economic incentivization; and residential DSM uptake can benefit from improved understanding of the interactions between human behavior, human outcomes, and energy use. Finally, this review investigates behavioral models and concludes that agent-based models are best suited for integrating these interactions into energy models, thereby driving the uptake of DSM, particularly in the important residential sector.

How flexible are energy flexibilities? Developing a flexibility score for revenue and risk analysis in industrial demand-side management

With rising and increasingly volatile energy prices, demand-side management (DSM) is becoming attractive within industry to optimize flexible electricity demand and to remain competitive. Despite economic and ecological benefits for companies, DSM implementation is not yet widespread. High effort in flexibility audits and complex optimization models to quantify DSM’s economic potentials represent initial barriers in practice, raising the question whether there is a simple way to analyze economic DSM potential as an initial indicator of successful DSM implementation that requires little data on flexibility characteristics. To address this need, this study develops a flexibility score that describes the degrees of freedom of individual energy flexibility measures (EFMs), i.e. how flexible a flexibility measure is. The flexibility score is tested and validated in a risk-return analysis on 46 real-world industrial EFMs from Germany optimized with integer linear programming (ILP). Analyzing Germany’s Day-Ahead and Intraday markets, the results show an increase in expected revenues and a simultaneous decrease in revenue risk with an increasing flexibility score, outlining DSM investments as attractive. Due to its ease of use, we see industrial companies, aggregators, energy service providers, and (energy) consultancy agencies as target users of the flexibility score. Policymakers can use the flexibility score to identify and design subsidies for energy flexibility investments.

Impact of demand-side management on the sizing of autonomous solar PV-based mini-grids

Solar PV-based autonomous mini-grids represent an economically affordable and robust electrification option for rural communities. However, the initial investment cost for renewable energy technologies such as solar PV remains high for rural communities. Implementation of demand-side management (DSM) could increase the cost-efficiency of mini-grids in rural areas. This requires demand-side knowledge, but little is still known of electricity demands in recently electrified areas and, in particular, of how DSM implementation could impact mini-grids. The few studies available focus either on systems or on appliance levels while this study aims to determine cost-efficiency impacts of DSM implementation at a category level. A shifting strategy is applied based on classification into high priority loads and low priority loads. Autonomous rural mini-grid components sizing for four different load categories and load flexibility are carried out using particle swarm optimization. The results show that different load category combinations result in large variations in terms of possible levelized energy cost reductions and, thus, in terms of the cost-optimal sizing of the mini-grid components. The DSM implementation on the household and productive use categories have the largest capacity of reducing the levelized energy cost, by 45.8% and 20.7%, respectively, compared to the no demand-side management case.

Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO2 emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).

Assessing the demand side management potential in biofuel production; A theoretical study for biodiesel, bioethanol, and biomethane in Germany

AbstractMinimizing the emissions produced during the processing of biofuel, one aim is to reduce or completely replace the amount of the required fossil fuels used for internal process energy. For the transition of process energy from fossil to renewable energy sources, such as solar and wind, the energy demand of biomass processing must be adjustable to the fluctuating electricity supply. The flexible adjustment of a system's power demand to follow the current power generation is commonly referred to as demand side management (DSM). This contribution shows the results of a study on the implementation of DSM in biofuel biorefineries. By identifying reference concepts that could represent biofuel production plants, the specific mass and energy consumption for the individual process steps in these reference concepts was analyzed through a literature study. The annual throughput and energy consumption of process steps in biofuel production could then be calculated, enabling the identification of the most energy‐consuming process steps. Subsequently, possible flexible operating load ranges of the respective process steps in biofuel production were identified. These findings allowed an assessment of the potential for different process units of biorefinery systems concerning the quantitative adaptability of the electricity load – the theoretical DSM potential. An approximate theoretical DSM potential of 146 MW has been identified for biofuel production in Germany. This cumulated theoretical DSM potential in biofuel production was compared to that of other industrial processes, demonstrating the magnitude and importance of the implementation of DSM in biofuel production. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems

The load requirement of a microgrid system fluctuates on an hourly manner. As the load demand rises and falls, utilities set various rates at different hours of the day, which is known as the time-of-usage (TOU) based electricity pricing. The two types of load that make up the microgrid system's hourly demand are elastic and inelastic. Demand-side management (DSM) restructures the entire demand model based on demand-price elasticity by moving elastic loads from peak load hours to those hours when the utility charges less. This research employs a hybrid intelligence method to reduce the total cost of three microgrid systems taking into account the implementation of DSM. Practical complexities like unit-commitment of the dispatchable fossil fuelled generators were considered. Numerical results project noticeable savings ranging from 8% to 18% for all the test systems when the DSM strategy was incorporated. A detailed comparative analysis with the results available in the literature corroborates the effectiveness of the proposed hybrid approach. Measures of central tendencies, non-parametric statistical analysis and algorithm execution time show better results of the hybrid optimization tool presented in this paper.

An Insight into the Integration of Distributed Energy Resources and Energy Storage Systems with Smart Distribution Networks Using Demand-Side Management

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints.

Advanced smart trigeneration energy system design for commercial building applications – Energy and cost performance analyses

Smart building-integrated trigeneration technology has the potential to meet the world goals for significant reduction of greenhouse gas (GHG) emissions. Some of the new research and development policies are aimed at development of novel and renewable hybrid systems, shift from unit to integration technology; implementation of demand-side management; move from centralized macro to decentralized micro technologies, etc. Recognizing the importance of trigeneration in future energy mix, hybrid renewable trigeneration systems - with integration of photovoltaic-thermal (PVT), ground source heat pump (GSHP) and smart control - were developed for a small commercial building. This paper discusses the energy and cost performance as well as GHG emissions of these systems sized to meet building space heating/cooling loads and HVAC electric power requirements. The results enhance the knowledge and provide useful information to designers on how these trigeneration systems should be applied under variety of conditions, what are their advantages in comparison to a single ground source heat pump system, and what are the energy, economic and GHG emission impacts of smart communications and artificial intelligence controls.

Managing the Demand in a Micro Grid Based on Load Shifting with Controllable Devices Using Hybrid WFS2ACSO Technique

Demand Side Management (DSM) is an effective tool for utilities through reducing the demand of peak load and controlling the utilization of the energy of the system. The implementation of DSM provides benefits for utilities and is profitable for the customers who are involved in this process. DSM based on a load shifting strategy is proposed in this paper by employing various devices to minimize the energy consumption pattern in the system. The proposed hybrid strategy is the joint implementation of the Wingsuit Flying Search Algorithm (WFSA) and Artificial Cell Swarm Optimization (ACSO). The searching behavior of WFSA is enhanced by ACSO. Hence, it is named the WFS2ACSO technique. The implementation of this load shifting technique was carried out on three different types of loads, these being residential loads, commercial loads, and industrial loads. Two case studies, over summer and winter, were validated to check the feasibility of the test system. The proposed method aimed to achieve the load demand in an effective way for the minimization of bill electrification, Peak to Average Ratio (PAR), and the consumption of power. The Time-of-Use (TOU) pricing was implemented to calculate the savings in energy bills. The proposed test system of the Micro Grid (MG) was executed on a MATLAB platform with two case studies based on the optimization methods WFSA and WFS2ACSO. Simulation results demonstrated the comparative analysis of electricity cost and peak load with different algorithms and were carried out with and without DSM consideration. The projected DSM methodology achieved considerable savings as the peak load demand of MG decreased. Furthermore, the decrease in PAR levels of 14% in the residential load, 16% in the commercial load, and 10% in the industrial load, with and without the DSM methodology, was presented. The flight length and awareness of probability tuning parameters make the proposed algorithm more effective in obtaining better results. The test results obtained prove the effectiveness of the hybridized algorithm as compared with other trend-setting optimization techniques such as Particle Swarm Optimization (PSO) and Ant Lion Optimization (ALO).

Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression (LR), auto-regressive integrated moving average (ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform (EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory (LSTM) method is trained for each sub-layer independently. In order to better tune the hyper-parameters, a Bayesian hyperparameter optimization (BHO) algorithm is adopted in this paper. Three case studies are de-signed to evaluate the performance of the proposed method. From the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.