Search

There is an increasing focus on the time at which energy is used in buildings both to reduce utility costs and carbon emissions in response to time-dependent grid signals. One method to shift electrical load out of peak pricing hours is to use batteries, but they have high first costs and also incur an energy penalty due to round trip efficiency and other losses. Another method is to use thermal storage to offset heating and cooling. Similarly, mechanical ventilation systems can also be controlled to shift energy use to periods of the day with lower energy, cost, and environmental impacts by varying the ventilation rate while still meeting ventilation code requirements. Mechanical ventilation systems in large multi-family residential buildings are mostly central air systems with either manually balanced dampers or constant airflow regulator (CAR) dampers that aim to provide a constant ventilation airflow rate to each apartment. ASHRAE 62.2 allows for dynamic ventilation rate systems in these buildings as long as the average relative exposure rate and the peak relative exposure rate during occupied periods are no more than 1 and 5, respectively, for any time interval that cannot exceed an hour. In this study, we used EnergyPlus simulations to examine energy end-use profiles for a large multi-family building under design in San Jose, California. We considered a balanced ventilation system using a central dedicated outdoor air supply (DOAS) system. We tested different load-shifting scenarios with multiple parameters to explore how the ventilation airflow rate can be varied to shift load, while also assessing energy and utility cost impacts. The parameters we assessed in each scenario were: the presence of a centralized ERV system or not; ventilation design sizing; and length of load shifting time period. All dynamic ventilation cases, with and without ERV systems, resulted in energy and operational cost savings relative to the constant ventilation cases when compared to providing the same amount of load shifting using batteries, and all tested strategies met ASHRAE 62.2 requirements. The results show that after accounting for the battery penalty typically associated with load shifting, all dynamic ventilation cases reviewed result in improved energy savings when compared to the constant ventilation strategy.

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives [ABSTRACT FROM AUTHOR]

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users' unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables. [ABSTRACT FROM AUTHOR]

Demand response is an effective solution for balancing supply and demand in modern energy supply systems. For utility or load aggregators, it is important to accurately target potential consumers to participate in demand response programs to recruit a massive number of users. This is especially important for the invitation-based demand response mode, which is currently often used in China. In this paper, a portrait-based method is proposed to effectively identify potential consumers for different demand response tasks based on historical loads. Eight indicators are proposed to quantify the energy consumption characteristics from different aspects, and an evaluation method is introduced. Then, a selection method based on the K-means clustering algorithm and support vector machine classifiers is proposed. The method is tested under two scenarios, including load shifting and monthly peak shaving. The results show that the proposed method can identify potential users effectively, and the accuracy of the trained classification model exceeds 99.9%. The proposed portrait-based identification method provides an effective way to describe users' energy consumption characteristics and select potential users effectively, which is very useful for helping the utility or virtual plant with load management. [ABSTRACT FROM AUTHOR]

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools.

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows' food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method's performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system's generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. [ABSTRACT FROM AUTHOR]

Copyright of Energy Policy Journal / Polityka Energetyczna is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research focused on the implementation of state-of-the-art system integration, involving a three-phase 540 KVA bidirectional inverter and a lithium-ion battery energy storage system with a capacity of 1.86 MWh, at the Florida Solar Energy Center (FSEC). The system was firstly put into use at the FSEC for load shifting. The second step was to add photovoltaic (PV) panels for peak shaving. The third step was to take the FSEC completely off-grid. The goal of this research was to provide a case study by implementing a power management algorithm, estimating the PV panel size, and presenting the test results and an analysis. Based on the power management algorithm, load shifting was achieved by using 180 kWh energy storage. To acquire peak shaving and the uniformity of power consumption, an 80 kW PV farm was sufficient. The optimal PV panel size was 500 kW for the off-grid scenario. [ABSTRACT FROM AUTHOR]

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

It is important to make improvements for the optimum operation of water pumping stations in order to reduce energy costs while meeting water demand continuously. In this article, a new algorithm (OSDPA-optimal simplified dynamic programming algorithm) has been developed by optimizing the previously developed SDPA (simplified dynamic programming algorithm) approach. The SDPA approach is most effective with straightforward systems, whereas the OSDPA approach is versatile, suitable for optimizing both simple and multi-stage, complex pumping systems. A water supply system with a multi-stage structure for Sakarya Water and Sewerage Administration (SASKİ) in Turkey has been used as a case study. The results show that the OSDPA approach can be quite successful in shifting the loads in the time period (peak) where the unit price is expensive to the time periods (night and day) where the unit price is cheaper. When compared with the conventional water level control (CWLC) approach, while SDPA saves about 18% for pump C in the second stage, about 22% for pump B, and about 19% for pump A in the first stage; OSDPA saved 39% for C pump, 41% for B pump and 24% for A pump. [ABSTRACT FROM AUTHOR]

In today's power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity. [ABSTRACT FROM AUTHOR]

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP's storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units. [ABSTRACT FROM AUTHOR]

Advances in energy storage technology have allowed the application of load shifting in the utility grid for a more efficient power system operation. However, the economy of the Battery Energy Storage System (BESS) application is not linear to the cost reduction obtained from excluding the costly generators. This paper presents the economic analysis of cost-based load shifting implementation and an approach to determine the generation units to be deactivated and replaced by BESS on three large-scale power grids in Indonesia: Sumbagut, Sumbagteng, and Sumbagsel systems. The economic feasibility is evaluated using the Net Present Value (NPV), considering the savings and the BESS investment cost. The result shows that the proposed approach is more robust than the conventional methods, such as removing the most expensive units, units with the highest cost reduction, units with the best economic return contribution, or the maximum number of units that can be replaced.

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users’ unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables.

Demand response is an effective solution for balancing supply and demand in modern energy supply systems. For utility or load aggregators, it is important to accurately target potential consumers to participate in demand response programs to recruit a massive number of users. This is especially important for the invitation-based demand response mode, which is currently often used in China. In this paper, a portrait-based method is proposed to effectively identify potential consumers for different demand response tasks based on historical loads. Eight indicators are proposed to quantify the energy consumption characteristics from different aspects, and an evaluation method is introduced. Then, a selection method based on the K-means clustering algorithm and support vector machine classifiers is proposed. The method is tested under two scenarios, including load shifting and monthly peak shaving. The results show that the proposed method can identify potential users effectively, and the accuracy of the trained classification model exceeds 99.9%. The proposed portrait-based identification method provides an effective way to describe users’ energy consumption characteristics and select potential users effectively, which is very useful for helping the utility or virtual plant with load management.

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. Two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

To reduce the electricity grid's valley—peak difference, thereby resulting in a smoother electricity load, this study employs a compressed CO2 energy storage system to facilitate load shifting. Load shifting by the CCES system not only enhances the energy flexibility of the electricity load but also creates energy arbitrage from variations in the electricity prices. An optimization model is developed to optimize the operation of the CCES system to minimize the standard deviation of the electricity load. Thereby, load shifting by the CCES system can be achieved. Based on the real electricity loads and prices, results indicate that, with an energy storage capacity of 267 MWh, the CCES system can provide 3845 MWh, 4052 MWh, and 3816 MWh of upward flexible energy and 3846 MWh, 3180 MWh, and 3735 MWh of downward flexible energy during a week in summer, winter, and the transition season, respectively. With a lifespan of 35 years, the CCES system can attain a net present value (NPV) of MUSD 239.9 and a payback time of 2 years. The sensitivity analysis shows that increasing the energy storage capacity of the CCES system augments both the upward and downward flexible energy of the electricity load but reduces the NPV of the CCES system. [ABSTRACT FROM AUTHOR]

Advances in energy storage technology have allowed the application of load shifting in the utility grid for a more efficient power system operation. However, the economy of the Battery Energy Storage System (BESS) application is not linear to the cost reduction obtained from excluding the costly generators. This paper presents the economic analysis of cost-based load shifting implementation and an approach to determine the generation units to be deactivated and replaced by BESS on three large-scale power grids in Indonesia: Sumbagut, Sumbagteng, and Sumbagsel systems. The economic feasibility is evaluated using the Net Present Value (NPV), considering the savings and the BESS investment cost. The result shows that the proposed approach is more robust than the conventional methods, such as removing the most expensive units, units with the highest cost reduction, units with the best economic return contribution, or the maximum number of units that can be replaced. [ABSTRACT FROM AUTHOR]

Demand-side management has become a viable solution to meet the needs of the power system and consumers in the past decades due to the problems of power imbalance and peak demand on the grid. This study focused on an improved decision tree-based algorithm to cover off-peak hours and reduce or shift peak load in a grid-connected microgrid using a battery energy storage system (BESS), and a demand response scheme. The main objective is to provide an efficient and optimal management strategy to mitigate peak demand, reduce the electricity price, and replace expensive reserve generation units. The developed algorithm is evaluated with two scenarios to see the behavior of the management system throughout the day, taking into account the different types of days (weekends and working days), the random profile of the users' demand, and the variation of the energy price (EP) on the grid. The simulation results allowed us to reduce the daily consumption by about 30% to 40% and to fill up to 12% to 15% of the off-peak hours with maximum use of renewable energies, demonstrating the control system's performance in smoothing the load curve.

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method’s performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system’s generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust.

The western region is a destination for the transfer of data center computing capacity. It is rich in natural resources and must take full advantage of its energy and climate advantages. In the context of "Channel Computing Resources from the East to the West", data centers urgently need to quantify the carbon reduction benefits during the process of transferring computing capacity. Therefore, this paper constructed a carbon emission accounting model for computing capacity transfer by considering three influencing factors: renewable energy, climate factor, and transfer process. To illustrate the effectiveness, a case of eight nodes of "Channel Computing Resources from the East to the West" was presented. The results show that compared with the carbon emission reduction from renewable energy and climate factors, the additional carbon emission caused by the transmission process is minimal, and for each 1 kW·h of computing capacity, 0.053~0.344kg of carbon emission can be reduced if only the first two factors are considered. To improve the carbon emission benefits from load shifting, the western region should guide data centers to gather in resource-intensive places, vigorously develop clean energy industries, increase the development of clean energy, and promote the degree of clean energy consumption, while grasping this opportunity to attract digital industries to land and promote the digital transformation of traditional industries. [ABSTRACT FROM AUTHOR]

This paper presents a step-by-step approach to assess the energy flexibility potential of residential consumers to manage congestion in the distribution network. A case study is presented where a selected transformer station exhibits signs of overloading. An analysis has been performed to evaluate the magnitude of the overloading and the timing of the overload occurrence based on their historical load data. Based on the historical load data, the four most prominent consumers have been chosen for the flexibility assessment. Temperature load dependency has been evaluated for the selected consumers. The paper's novel approach focuses on selecting individual consumers with the highest energy flexibility potential, and analysing their load patterns to address transformer overloading. To achieve this, machine learning algorithms, specifically, multiple linear regression and support vector machines, were used for load profile forecasting during the overload occurrences. Based on the forecast and measured load patterns, flexibility scenarios were created for each consumer. The generated models were evaluated and compared with the forecasting based on the average load of the past days. In the results, three potential consumers were identified who could resolve the transformer overloading problem. The machine learning models outperformed the average-based forecasting method, providing more realistic estimates of flexibility potential. The proposed approach can be applied to other overloaded transformer stations, but with a limited number of consumers. [ABSTRACT FROM AUTHOR]

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP’s storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units.

This study investigated a grid-connected smart microgrid (MG) system integrating solar photovoltaic (PV) panels and a battery energy storage system (BESS) as distributed energy resources (DERs) to locally serve residential loads. The load-shifting demand-side management (DSM) technique was employed to effectively manage the load appliances. The proposed load-shifting algorithm relies on minimum price incentives to allow customers to allocate their load appliances economically during minimum price periods. The algorithm considers the waiting times and minimum tariff periods for appliances, calculates precise operating durations for each appliance, and prioritizes powering the appliances from the MG first, followed by the main grid. The system comprises two non-shiftable and three shiftable loads. When the MG power is insufficient to activate all shiftable loads, the system transfers the remaining unsupplied shiftable appliances to periods with low-priced energy. The Energy Internet concept is adopted to manage energy and monitor usage when a customer is unable to check the accuracy of their energy meter by supervising the system's features on-site. The proposed comprehensive system enables load management, continuous monitoring, customer awareness, and energy cost saving. Six cases were studied, both numerically and experimentally, with varying MG power generation and load pre-scheduling periods, with and without DSM application. In all adopted cases, the implemented system save energy costs by at least 50%. [ABSTRACT FROM AUTHOR]

One of the most vital issues in electrical systems involves optimally operating microgrids (MGs) using demand-side management (DSM). A DSM program lowers utility operational costs in one sense but also needs policies that encourage financial incentives in the other. The present study formulates the optimum functioning of MGs using DSM in the form of a problem of optimization. DSM considers load shifting to be a viable option. There are operational limitations and executive limitations that affect the problem, and its objective function aims at minimizing the overall operational prices of the grid and the load-shifting prices. The major problem has been solved using an improved butterfly optimization scheme. Furthermore, the suggested technique was tested in various case studies that consider types of generation unit, load types, unit uncertainties, grid sharing, and energy costs. A comparison was made between the suggested scheme and various algorithms on the IEEE 33-bus network to demonstrate the proficiency of the suggested scheme, showing that it lowered prices by 57%. [ABSTRACT FROM AUTHOR]

Industrial facilities are seeking new strategies that help in providing savings mechanisms for demand charges. Demand charges are the charges incurred by industrial facilities as a result of power usage. Thermal energy storage has advanced significantly with lots of new applications, garnering the interest of many industrial facilities. These applications could be used to shave the industrial facilities' peak electric demand and reduce their demand charges. This paper aims to demonstrate the efficacy of thermal energy storage in reducing demand charges and highlight new developments in the integration of smart control systems with thermal energy storage. The study compares energy consumption and peak demand for a facility equipped with and without thermal energy storage tanks using a fixed schedule for charging and discharging. Additionally, the paper examines the impact of incorporating a smart controller to determine when to charge and discharge the tank based on the facility's real-time power usage and a given setpoint. The results indicate cost savings from the use of thermal energy storage tanks under two proposed scenarios, reflected in the reduced cost of power consumption for the studied facility. The incorporation of a smart controller with the thermal energy storage tank in the facility studied could provide estimated savings of 3.3% per year of power consumption charges, without considering the contribution of any incentives. The estimated savings provided by the fixed schedule scenario are 2.7% per year. [ABSTRACT FROM AUTHOR]

In response to the imperative of the climate crisis, California is in the midst of deployment of low-carbon energy generation and electrified heating and transportation at a rapid pace. In 2018 the grid managed by the California Independent System Operator (CAISO) was already generating 26 percent of its power from non-hydro renewable sources, with an additional ten percent hydroelectric power; and with SB 100, California statute now sets a target of 100 percent carbon free electricity by 2045. Maintaining this fast pace of decarbonization requires action across multiple domains on the grid from generators to loads. This study describes new findings on the scale of the opportunity for enabling load shifting as a form of demand response (DR) in California, as part of this renewable energy transition. We use the shorthand term Shift to refer to this potential load-shifting DR resource. The operating principles for the electric power system mean that achieving California’s renewable energy goals requires careful planning to build and operate the system in a way that maintains reliability at low cost. The most fundamental of these principles is that generation and demand need to be balanced at all times. Historically this has meant building adequate flexible and dispatchable generators that can be operated to match inflexible and uncontrolled loads. Meeting large fractions of demand with variable renewable energy (VRE) generation introduces a new paradigm, exemplified by the “duck curve” in California. In the times of year it occurs, the duck curve illustrates the net electricity demand that must be met by non-VRE generation; it has sharp peaks in morning and evening (the “tail” and “head” of the duck, respectively) and steep transitions to a deep midday trough (the duck’s “belly”) resulting from solar generation. Balancing the duck curve is a key priority for integrating increasing levels of VRE on the California grid. Shift DR is one of several renewables integration planning approaches that can work together to balance a high-VRE system. Others include “overbuilding” renewable energy with the intent to curtail during times of surplus generation, building and operating transmission lines to integrate regional systems, and building and operating energy storage to match generation with loads. Based on the 2019-2020 Integrated Resource Planning (IRP) model for California1—which did not consider Shift among its resource options—the least-cost pathway for achieving California GHG goals includes all of these other approaches. The Reference System Portfolio that is used to inform utility planning includes development of energy storage that is operated to serve about 10 GWh of consumption on the average day in 2020, 70 GWh in 2030, and 400 GWh by 2045. There is also significant curtailment of VRE in this least-cost portfolio, with 4 GWh on the average day in 2020, rising to 15 GWh by 2030 and 100 GWh by 2045. Throughout this report we compare the Shift resource to curtailment and storage as benchmarks, providing context for the results we report related to the quantity, cost, and performance of Shift. The timing of evening peak and curtailment defines times when load shifting is most valuable and would likely be used. The quantity of curtailment and use of storage to balance the system represent how much Shift could be valuable at the upper bound if Shift is inexpensive. The cost of energy storage is also a useful benchmark for the cost of Shift because it is the most comparable “competing” technology option; we therefore use the cost of storage to set a reference price level below which Shift will be most broadly cost competitive.

In the rural regions of southern Brazil, electricity is largely directed to irrigation activities on rice crops at restricted periods of the year. Typically, customers in these regions are called "irrigators", and have some characteristics different from loads in urban centers, such as high demand levels and sharp load variations. These characteristics can result in problems of excessive loading on distribution grids at certain times of the day, generating concerns for the power utilities in relation to the security of the electrical system, energy supply to customers, and the integrity of electrical equipment. An alternative to avoid or mitigate these possible problems may be the application of a demand management model to irrigator customers. In this context, a load shifting strategy can be inserted to reduce demand in more critical periods and move it to intervals with lower load on the power grid. In this context, this article presents a demand-side management methodology in distribution systems located in rural areas, employing the load shifting strategy for irrigator customers. The methodology proposed in this paper is not an entirely novel approach, but one specifically developed for the context of irrigator customers, a subject little studied in the literature. The load management model proposed by this study is segmented into three hierarchical levels. The first level is the identification of the electrical characteristics of the distribution systems, the second level is the power flow analysis of the distribution networks, and the third and last level consists in the application of load shifting to the irrigator customers of these electrical systems. The load shifting strategy is modeled by a linear programming algorithm and is only applied to irrigator customers in situations of excessive loading on power grid. The case studies were conducted on three distribution systems of a power utility, with more than 150 irrigator customers. The DSM model based on the load shifting strategy reduced the maximum demand and daily load variations on the three rural feeders evaluated. The proposed changes in load patterns can ensure the continuity of electric power supply service in future even with the high concentration of load on distribution networks, benefiting customers and power utilities. [ABSTRACT FROM AUTHOR]

Power demand of isolated villages where on‐grid power supply is not economical can be met by standalone renewable energy system. Hybrid renewable energy system (HRES) considered in the current study involves photo‐voltaic, wind turbine, diesel generator, and battery. In the present study, the demand side energy management methods are used for energy management. The load shifting and strategic conservation methods are used. The energy management schemes are applied in the stage of load estimation. It is observed in the analysis that, the net present cost (NPC) of $55 263 is reduced to $34 009 with the application of demand side energy management strategies. The two control strategies, load‐following and predictive‐dispatch strategy are incorporated. The three types of batteries like lead‐acid, lithium‐ion, and zinc‐bromide are integrated, one at a time, for analysis. The analysis results in the optimal HRES configuration solution with the combination of both load shifting and strategic conservation techniques of demand side energy management. The NPC is reduced by 37% in comparison with the HRES without the application of energy management methods. The zinc‐bromide battery integrated HRES configuration through predictive dispatch control strategy with the application of demand side energy management schemes results in optimal solution for electrification of cluster of villages located in Uralagallu panchayat, Sagar Taluk, Shivamogga District, Karnataka, India. To the similar kind of geographical condition areas, the same HRES configuration with similar approach can be extended for electrification. [ABSTRACT FROM AUTHOR]

This paper examines a Renewable Energy Community (REC) made up of 10 dwellings that collectively self-consume energy produced by a photovoltaic field connected to a water purifier. Each dwelling heat demand is satisfied by means of Heat Pump (HP) coupled with Thermal Energy Storage (TES), which can be managed to perform load shifting and increase collectiveself-consumption (CSC). Techno-economic analyses are performed accounting for HPs' COP variation with temperature and part load operations, as well as TES heat dispersion. A new centralised control strategy for HPs is proposed and a sensitivity analysis is performed to assess the impact of varying TES system capacity. The results show that the centralised strategy can increase the CSC by 12-30%, with TES sizes of 100-1000 litres respectively. But the electricity consumption of HPs increases by 2-5% due to higher storage system temperatures causing worse average COPs by 2.3-0.6% and higher thermal losses by 29-58%. As a result, REC's energy independence rise, as does the amount of CSC incentives, but electricity bills also increase. Comparing these trends shows that CSC incentives should be adjusted according to energy prices to ensure cost-effective outcomes for all stakeholders and encourage the adoption of similar centralised control strategies. [ABSTRACT FROM AUTHOR]

In the process of development and utilization of a large-scale borehole heat exchanger (BHE) array system, the phenomenon of load shifting within BHE array can be observed. In this paper, OpenGeoSys software coupled with TESPy toolkit is used to establish a comprehensive numerical model of BHE system (without depicting the heat pump part), and the behaviors of load shifting between BHEs with different design parameters are studied. The results show that the outlet temperature of single BHE and BHE array is generally rising, and the soil temperature around the BHE has accumulated unbalanced heat. The soil temperature near the BHEs array fluctuates more obviously than the single BHE system, and the distribution is uneven. At the end of the 15th year, the soil temperature near the center BHE increased by 2 °C compared with the initial soil temperature, which was more favorable in winter, but was not conducive to the performance improvement in summer. Further analysis by changing the inter-borehole spacing shows that with the increase of the inter-borehole spacing, the load shifting behaviors are gradually weakened, and the maximum shifted load of the central BHE is linear with the change of the inter-borehole spacing. After changing the layout methods, we observe that the more intensive the layout is, the more load shifting behavior is, and the unbalanced rate of soil temperature distribution around the linear layout is lower than other layouts. With the increase in the number of BHEs, the load shifting behaviors are further enhanced. By analyzing the proportion of shifted load amount relative to the average value, it is found that the system will take a longer time to reach heat balance with the increase of BHEs' number. A shutdown of part of BHEs for a certain period of time will help to improve the long-term operational efficiency of the large-scale shallow ground source heat pump (GSHP) system. [ABSTRACT FROM AUTHOR]

Reducing our carbon footprint is one of the biggest challenges facing humanity in the current millennium. In the last few years, researchers have focused their attention on balancing the demand and supply, thereby allowing better management of renewable energy resources. In this regard, many energy management strategies have been developed. Nevertheless, testing, evaluating, and comparing such approaches in multiple scenarios, and above all, assessing their generalization, is currently a hard, or even impossible, task. Furthermore, analyzing the impact of such strategies in Energy Communitys (ECs) is an underexplored task. This is due to the lack of existing EC datasets and simulators that allow users to evaluate and compare their approaches. Although there are some tools to generate demand and production profiles, they are all developed with a single purpose. To address these challenges, PROCSIM is presented: an open-source simulator designed especially to create energy community datasets for multiple purposes—in particular, to test and evaluate different algorithms and models. It includes integration with a consumption-profiles generator, tools to simulate Solar Photovoltaic (PV) and wind production, a module that generates an EC dataset, and finally, a set of metrics to evaluate the generated community. To conclude, a case study comprised of two experiments is presented. The first experiment shows how an EC dataset can be created using PROCSIM. In the second experiment, an exemplification of how this dataset can be used to evaluate an optimization algorithm is provided, namely, to optimize the control of a battery. Ultimately, it is shown that the simulator can generate energy community power demand and generation scenarios. The scenarios can be fully customized by the user, considering different sizes (power capacity) and numbers of assets, and diverse generation/consumption characteristics. The datasets generated by PROCSIM can be useful for different purposes, such as optimal scheduling of EC generation resources and consumption flexibility, and for designing battery energy storage systems. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) has a wide impact on many potential applications. The impact of IoT on performing demand-side management (DSM) in an Indian educational institution has not been researched in depth before. In this research work, an IoT-enabled SDSMS (Smart DSM System) has been deployed with the main objective of minimizing electricity tariff and also to tweak the quality of user comfort. It can be feasible by prioritizing available renewable PV solar energy during peak hours in an Indian educational institution. DSM has been performed using day-ahead load shifting and rescheduling the different classes of institutional loads by applying hybrid BPSOGSA (Binary Particle Swarm Optimization and Gravitational Search Algorithm). The BPSOGSA performance on DSM has been evaluated based on electricity tariff, peak demand range, and PAR and compared with the outcomes of both binary conventional algorithms BPSO and BGSA, respectively. The proposed method enhances the degree of user comfort (DUC) by tripping the operation of non-critical institutional loads. Simulation results obtained using MATLAB corroborate that BPSOGSA outperforms both BPSO and BGSA under both DSM scenarios. Before DSM, Peak demand, PAR, and Electricity tariffs were found to be 1855.47 kW, 4.1286, and $2030.67 while after DSM, they reduced to 1502.24 kW, 3.263, and $1314.40 respectively. This indicates a 35.273% reduction in electricity tariff, a 19.037% scale down in peak demand, and a 20.97% reduction in PAR. Finally, the real-time IoT-based SDSMS hardware is implemented at the Renewable energy laboratory for real monitoring of energy consumption via the Blynk application. [ABSTRACT FROM AUTHOR]

This paper examines a Renewable Energy Community (REC) made up of 10 dwellings that collectively self-consume energy produced by a photovoltaic field connected to a water purifier. Each dwelling heat demand is satisfied by means of Heat Pump (HP) coupled with Thermal Energy Storage (TES), which can be managed to perform load shifting and increase collective-self-consumption (CSC). Techno-economic analyses are performed accounting for HPs' COP variation with temperature and part load operations, as well as TES heat dispersion. A new centralised control strategy for HPs is proposed and a sensitivity analysis is performed to assess the impact of varying TES system capacity. The results show that the centralised strategy can increase the CSC by 12-30%, with TES sizes of 100-1000 litres respectively. But the electricity consumption of HPs increases by 2-5% due to higher storage system temperatures causing worse average COPs by 2.3-0.6% and higher thermal losses by 29-58%. As a result, REC's energy independence rise, as does the amount of CSC incentives, but electricity bills also increase. Comparing these trends shows that CSC incentives should be adjusted according to energy prices to ensure cost-effective outcomes for all stakeholders and encourage the adoption of similar centralised control strategies.

We use a national Saudi power system dispatch optimization model to assess the implications of load shifting from the industrial sector. This model quantifies the fuel offtake, emission, and cost implications of shifting industrial loads from peak summer to off-peak winter months with industrial electricity tariffs and production levels unchanged. Then, the power sector results are used to assess macro implications –in the form export revenues. Our analysis shows that load shifting can save between $7.9 million and $17.7 million per year at the prevailing administered fuel prices and could reduce carbon dioxide emissions by up to 584 thousand tons. Furthermore, the total cost savings range from $127.2 million to $239.4 million per year when the opportunity costs of saved fuel (mostly crude oil) are considered. These potential savings represent 8.1% of the annual fuel costs of the system. Furthermore, we recommend shifting at least 15% of total industrial load to increase fuel savings if natural gas prices are reformed. We conclude by discussing potential policy enablers and barriers to adopting load shifting in Saudi Arabia.

The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using two-layer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in first-layer optimization. Then, minimizing the consumption costs and consumers’ discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lp-metric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the two-layer optimization is confirmed using testing proposed case studies in the numerical simulation.

Increasingly large amounts of electric supply and load are being deliberately operated or sited on the basis of marginal emissions factor (MEF) models. Validating and calibrating such models is therefore of growing policy importance. This paper uses a natural experiment involving variation in relative changes in wind generation potential at wind farms in the ERCOT power grid to create a benchmark MEF and examine the relative accuracy of several common classes of short term MEF models. This work focuses on MEFs at the level of a few individual generating nodes, a much smaller geographic scale than the Balancing Authority (BA) or load zone. Additionally, the use of wind generation potential as a regressor allows us to factor in wind curtailment, in contrast to previous work. We evaluate multiple prevalent existing MEF models and find that both dispatch and statistical MEF models have a high degree of agreement with the benchmark MEF, while heat rate and average emissions do not. We also find that the emissions reduction benefits of optimizing electricity with MEFs using a geographically granular model instead of a BA-wide model are 1.4, 1.3 and 1.5 times larger for dispatch, statistical and heat rate models, respectively.

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.

To reduce the electricity grid’s valley—peak difference, thereby resulting in a smoother electricity load, this study employs a compressed CO2 energy storage system to facilitate load shifting. Load shifting by the CCES system not only enhances the energy flexibility of the electricity load but also creates energy arbitrage from variations in the electricity prices. An optimization model is developed to optimize the operation of the CCES system to minimize the standard deviation of the electricity load. Thereby, load shifting by the CCES system can be achieved. Based on the real electricity loads and prices, results indicate that, with an energy storage capacity of 267 MWh, the CCES system can provide 3845 MWh, 4052 MWh, and 3816 MWh of upward flexible energy and 3846 MWh, 3180 MWh, and 3735 MWh of downward flexible energy during a week in summer, winter, and the transition season, respectively. With a lifespan of 35 years, the CCES system can attain a net present value (NPV) of MUSD 239.9 and a payback time of 2 years. The sensitivity analysis shows that increasing the energy storage capacity of the CCES system augments both the upward and downward flexible energy of the electricity load but reduces the NPV of the CCES system.

Incorporating demand-side management (DSM) into residential energy guarantees dynamic electricity management in the residential domain by allowing consumers to make early-informed decisions about their energy consumption. As a result, power companies can reduce peak demanded power and adjust load patterns rather than having to build new production and transmission units. Consequently, reliability is enhanced, net operating costs are reduced, and carbon emissions are mitigated. DSM can be enhanced by incorporating a variety of optimization techniques to handle large-scale appliances with a wide range of power ratings. In this study, recent efficient algorithms such as the binary orientation search algorithm (BOSA), cockroach swarm optimization (CSO), and the sparrow search algorithm (SSA) were applied to DSM methodology for a residential community with a primary focus on decreasing peak energy consumption. Algorithm-based optimal DSM will ultimately increase the efficiency of the smart grid while simultaneously lowering the cost of electricity consumption. The proposed DSM methodology makes use of a load-shifting technique in this regard. In the proposed system, on-site renewable energy resources are used to avoid peaking of power plants and reduce electricity costs. The energy Internet-based ThingSpeak platform is adopted for real-time monitoring of overall energy expenditure and peak consumption. Peak demand, electricity cost, computation time, and robustness tests are compared to the genetic algorithm (GA). According to simulation results, the algorithms produce extremely similar results, but BOSA has a lower standard deviation (0.8) compared to the other algorithms (1.7 for SSA and 1.3 for CSOA), making it more robust and superior, in addition to minimizing cost (5438.98 cents of USD (mean value) and 16.3% savings). [ABSTRACT FROM AUTHOR]