Flexible Loads Research Articles

Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example.

Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) causes renewable power curtailment and weak operational performance. The regulation potential of flexible buildings is an effective method for handling this problem. This paper builds a regulation model of flexible heat load according to the dynamic heat characteristics and heat comfort elastic interval of the buildings, as well as a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates flexible regulation of multiple loads and a variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanisms of different types of loads. Its results show a saturation effect between heat and electrical loads in increasing renewable energy consumption and a synergistic effect in decreasing the operating cost. This synergy can reduce the operating cost by 0.73%. Furthermore, the operating cost can be reduced by 15.13% and the curtailment rate of renewable energy can be decreased by 12.08% when the flexible electrical and heat loads are integrated into the operation optimization of HRIES.

Flexible Load Optimization Operation Method for Integrated Energy Systems in Parks Considering Demand Response

The ongoing advancements in energy technology are progressively unveiling the high energy utilization benefits of comprehensive energy systems (CIES) in parks. Based on the concept of an energy hub, a CIES model including distributed power supply, gas turbine, and flexible loads is constructed by combining the characteristics of flexible loads in the park that can be leveled, shifted, and curtailed. In light of the demand response mechanism, a method for the low-carbon economic optimization operation of CIES has been developed, focusing on minimizing the total cost. This method utilizes the CPLEX solver to process the algorithm. The approach involves a comparative analysis of load distribution before and after engaging in demand response activities. Such analysis is crucial in understanding the influence of the demand fluctuations mechanism and in determining the most efficient method for adjusting flexible loads. This adjustment aims to further reduce the system costs of CIES. The findings indicate that, under the demand response mechanism, the involvement of flexible loads in CIES operations can significantly enhance the system’s economic efficiency.

A hierarchical structure for harnessing the flexibility of residential microgrids within active distribution networks: Advancing toward smart cities

The increasing integration of Renewable Energy Sources (RESs) and the replacement of fossil fuel vehicles with electric types in distribution systems, while enhancing economic and environmental aspects, have introduced new challenges to network production-consumption balance due to the inherent randomness of these resources. This factor has magnified the significance of balancing markets in renewable-based distribution networks, as these markets address uncertainties and are vital for operational stability. Consequently, this paper introduces a two-stage multi-layer methodology for organizing local energy and balancing markets among Residential Microgrids (RMGs) within an Active Distribution Network (ADN). The AND operator organizes both energy and balancing markets.In the first stage, the intra-day energy market operates, engaging non-profit-seeking aggregators, RMGs, and ADN. Moving to the second stage, the intra-hour balancing market is conducted involving profit-seeking Flexible Load (FL) and Electric Vehicle (EV) aggregators, RMGs, and ADN. The ADN operator organizes both markets and can alter the network topology online within the energy market. The planning of profit-seeking aggregators is formulated using Karush-Kuhn-Tucker (KKT) conditions and Strong Duality Theory (SDT) in the form of Mathematical Programming with Equilibrium Constraints (MPEC), considering the correlation of aggregators' profit with the satisfaction index of end-users. The simulation results of the proposed methodology on a modified 118-bus IEEE distribution test system using GAMS indicate that this approach not only provides a significant share of balancing capacities through FLs and EVs but also ensures system stability against intra-hour fluctuations across various scenarios. The results also validate that the proposed methodology achieves a reduction of 24.8 % in power losses and 5.06 % in market costs, while simultaneously ensuring profitability for both aggregators and RMGs.

Optimized low carbon scheduling strategy of integrated energy sources considering load aggregators

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES.

Understanding the role and design space of demand sinks in low-carbon power systems

As the availability of weather-dependent, zero marginal cost resources such as wind and solar power increases, a variety of flexible electricity loads, or ‘demand sinks’, could be deployed to use intermittently available low-cost electricity to produce valuable outputs. This study provides a general framework to evaluate any potential demand sink technology and understand its viability to be deployed cost-effectively in low-carbon power systems. We use an electricity system optimization model to assess 98 discrete combinations of capital costs and output values that collectively span the range of feasible characteristics of potential demand sink technologies. We find that candidates like hydrogen electrolysis, direct air capture, and flexible electric heating can all achieve significant installed capacity (>10% of system peak load) if lower capital costs are reached in the future. Demand sink technologies significantly increase installed wind and solar capacity while not significantly affecting battery storage, firm generating capacity, or the average cost of electricity.

Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

Optimizing integrated energy systems using a hybrid approach blending grey wolf optimization with local search heuristics

Integrated energy systems play a crucial role in the transition to sustainable energy solutions, requiring a delicate balance between economic efficiency and reliability. Energy storage and demand response are pivotal components for managing power fluctuations and optimizing consumption. This paper introduces a hybrid approach that combines Grey Wolf Optimization (GWO) with Local Search Heuristics (GWOLSH) to achieve optimal energy system configurations. The approach formulates a hybrid energy storage capacity allocation method that considers the needs of wind power companies and users. It begins by constructing load-shifting and load-reduction models based on flexible user-side load response characteristics. In a comparative analysis, GWOLSH exhibits superior performance over WSO and PSO, evidenced by achieving a lower cost reduction of 330,595 USD (GWOLSH) compared to 344,974 USD (WSO) and 350,694 USD (PSO), along with higher percentage improvements in system stability (0.38 % for GWOLSH, 0.39 % for GWO, and 0.40 % for Particle swarm optimization (PSO)) and greater user satisfaction levels (0.8985 for WSOLSH, 0.8930 for GWO, and 0.8928 for PSO). This hybrid configuration proves effective in enhancing both economic efficiency and system reliability, offering a promising solution for maintaining a consistent power supply amid fluctuations, identifying energy system configurations that enhance economic efficiency while bolstering system reliability to ensure a consistent power supply amid fluctuations.

Day-Ahead and Intraday Two-Stage Optimal Dispatch Considering Joint Peak Shaving of Carbon Capture Power Plants and Virtual Energy Storage

The anti-peaking characteristics of a high proportion of new energy sources intensify the peak shaving pressure on systems. Carbon capture power plants, as low-carbon and flexible resources, could be beneficial in peak shaving applications. This paper explores the role of carbon capture devices in terms of peak shaving, valley filling, and adjustment flexibility and constructs a virtual energy storage model utilizing various flexible loads on the demand side. Also, it proposes a joint peak shaving strategy involving carbon capture devices and virtual energy storage. Considering the predictive error characteristics of wind power and load across different time scales, this study establishes a day-ahead and intraday two-stage rolling regulation peaking model based on carbon capture power plants and virtual energy storage to fully leverage the diverse response speeds and peak shaving capabilities of various types of flexible loads. Initially, the model calculates the net load curve after implementing a demand response system, aiming to minimize the load peak–valley difference. Subsequently, within the intraday period, it seeks to minimize system operating costs by precisely allocating peak shaving resources as per demand, thus aiming for economic efficiency while ensuring the system’s peak shaving capability.

Operational performance of PCM embedded radiant chilled ceiling using a rule-based control strategy

The increasing energy demand for space cooling, projected to triple by mid-century and surpassing a quarter of today's global electricity usage, underscores the urgent need for innovative, more energy-efficient air-conditioning technologies. The research introduces phase change material (PCM) embedded radiant cooling (PCM-RCC) as a promising solution, offering enhanced energy efficiency and indoor environmental quality. Despite its potential, PCM-RCC remains in the developmental stage, requiring further research to fully unlock its benefits. This work aims to analyse the operational performance of PCM-RCC and develop an advanced rule-based control strategy to enhance its efficiency further. The analysis was conducted using a validated PCM-RCC transient system simulation model. To quantify the advantages of PCM-RCC over typical radiant cooling and evaluate the effectiveness of the proposed control strategy, two reference cases are defined: (1) basic-controlled PCM-RCC, and (2) radiant cooling without PCM. Results reveal that PCM-RCC with advanced control demonstrates higher load flexibility (∼93% off-peak time operation), 12% less electricity usage, a 5% coefficient of performance improvement, and a 30% decrease in operational costs compared to radiant cooling. Furthermore, the proposed system shows 9% lower energy usage with a 20% improvement in thermal comfort compared to basic-controlled PCM-RCC.

Using electrolytic hydrogen production and energy storage for balancing a low carbon electricity grid: Scenario assessments for India

Nuclear reactors and variable renewables will play a significant role in the global energy transition as providers of low carbon electricity to various end use sectors. Real time balancing of power demand and supply without modulation or curtailment is possible using electrolytic hydrogen plants and energy storage systems. The generation mix adopted and load profiles are unique to a country and this study considers the specific case of India. This work analyses the use of grid connected water electrolysers, grid scale battery storage, hydrogen storage and fuel cells as flexible loads and dispatch schemes for grid balancing. Based on postulated long term power generation scenarios for India, the minimum required system sizes for grid balancing are estimated and techno-economic uncertainties are assessed. The use of water electrolysers is prioritized to make use of excess power, while minimizing battery storage requirement. This scheme can potentially produce a substantial share of low carbon hydrogen in India for use in industrial decarbonization, thus reducing the need for additional generation infrastructure.

Optimizing System Resources and Adaptive Load Balancing Framework Leveraging ACO and Reinforcement Learning Algorithms

In today's constantly changing computer settings, the most important things for improving speed and keeping stability are making the best use of system resources and making sure that load balancing works well. To achieve flexible load balancing and resource optimization, this study suggests a new system that combines the Ant Colony Optimization (ACO) and Reinforcement Learning (RL) methods. The structure is meant to help with the problems that come up when tasks and resource needs change in big spread systems. ACO is based on how ants find food and is used to change how jobs are distributed among computer nodes based on local knowledge and scent tracks. This autonomous method makes it easy to quickly look for solutions and adjust to new situations. In addition to ACO, RL methods are used to learn about and adjust to how the system changes over time. By planning load balancing as a series of decisions, RL agents are able to keep improving their rules so that the system works better and resources are used more efficiently. Agents learn the best ways to divide up tasks and use resources by interacting with the world and getting feedback. The suggested system works in a spread way, which makes it scalable and reliable in a variety of settings. The system changes its behavior on the fly to react to changing tasks and resource availability by using the group intelligence of ACO and the flexibility of RL. The system can also handle different improvement goals and limitations, which makes it flexible and usable in a range of situations. The suggested approach works better than standard load balancing methods at improving system performance, lowering reaction times, and making the best use of resources, as shown by the results of experiments. Using the strengths of the ACO and RL algorithms, this structure looks like a good way to deal with the complexity of current computer systems and make good use of resources in changing settings

Deep reinforcement learning based dynamic pricing for demand response considering market and supply constraints

This paper presents a Reinforcement Learning (RL) approach to a price-based Demand Response (DR) program. The proposed framework manages a dynamic pricing scheme considering constraints from the supply and market side. Under these constraints, a DR Aggregator (DRA) is designed that takes advantage of a price generator function to establish a desirable power capacity through a coordination loop. Subsequently, a multi-agent system is suggested to exploit the flexibility potential of the residential sector to modify consumption patterns utilizing the relevant price policy. Specifically, electrical space heaters as flexible loads are employed to cope with the created policy by reducing energy costs while maintaining customers' comfort preferences. In addition, the developed mechanism is capable of dealing with deviations from the optimal consumption plan determined by residential agents at the beginning of the day. The DRA applies an RL method to handle such occurrences while maximizing its profits by adjusting the parameters of the price generator function at each iteration. A comparative study is also carried out for the proposed price-based DR and the RL-based DRA. The results demonstrate the efficiency of the suggested DR program to offer a power capacity that can maximize the profit of the aggregator and meet the needs of residential agents while preserving the constraints of the system.

A Comprehensive Investigation of Linear and Nonlinear Beam Models on Flexible Wind Turbine Blade Load Calculations

This study was performed to investigate the effects of structural nonlinearity and large deformations on the aeroelastic loads of flexible wind turbine blades. First, a blade structural analysis model was established using the geometrically exact beam (GEB) theory. Subsequently, the blade element momentum (BEM) theory was corrected using the geometrically exact method leading to the development of a geometrically exact blade element momentum (GE-BEM) model. The results from the GE-BEM model indicated that flapwise deformations always reduce blade fatigue loads, while torsional deformations decrease fatigue loads under low wind speeds but increase them under high wind speeds. Finally, the linear Euler–Bernoulli beam and the GEB were compared to explore the influence of geometric nonlinearity on the blade aeroelastic loads, which revealed that the Euler beam model underestimates the blade loads. The simulations that used the GEB model produced torsional root twist fatigue loads that were 57.49% greater than those generated when the Euler beam model was used. Furthermore, the flapwise bending moment fatigue loads at the root were 8.24% greater than those obtained by the Euler beam model. The smallest discrepancy between the results of the two models was 7.26%, and it corresponded to the edgewise fatigue load.

Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

As machine learning workloads are significantly increasing energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry-standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.

Sustainability of Data Center Digital Twins with Reinforcement Learning

The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent design and control of DC components such as IT servers, cabinets, HVAC cooling, flexible load shifting, and battery energy storage are essential. However, the complexity of designing and controlling them in tandem presents a significant challenge. While some individual components like CFD-based design and Reinforcement Learning (RL) based HVAC control have been researched, there's a gap in the holistic design and optimization covering all elements simultaneously. To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs. It is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. Furthermore, in its default setup, DCRL-Green provides a benchmark for evaluating single as well as multi-agent RL algorithms. It easily allows users to subclass the default implementations and design their own control approaches, encouraging community development for sustainable data centers. Open Source Link: https://github.com/HewlettPackard/dc-rl

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

Multi-objective optimization scheduling method considering flexible load integration for distribution network

In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Forecast-based stochastic optimization for a load powered by wave energy

Stand-alone renewable energy systems, which harness and use electricity without connection to a distribution grid, can benefit from using resource forecasts when making real-time decisions on how to manage power. An example of this is an ocean observation system that uses a wave energy converter (WEC) and battery bank to produce and store electricity in order to meet electrical load requirements for oceanographic sensors and instrumentation. By using wave forecasts to optimize the power consumption of this system, it is possible to reduce the size of the WEC and battery bank needed, thereby reducing the overall system’s cost and complexity. In this paper, we assess the benefits of forecasting for a generic wave-powered ocean observation system. We model an observation platform powered by a WEC and battery-storage system that can switch between four power states, full power: 600 W, medium power: 450 W, low power: 45 W, and survival mode: 1 W. To determine which power state to enter over time, we present a stochastic optimization method that interprets wave forecasts and system information to select a power state on an hourly basis and simulate over two months of consecutive decisions. Using this simulation framework, we compare 8 power management strategies across a range of WEC sizes (3–5 m) and battery capacities (2.5–35 kWh). We find that it is possible to maintain full (600 W) power consumption over the entire simulation window with a 5 m diameter WEC and 25 kWh battery bank, which is on the upper end of the ranges evaluated. To employ a smaller WEC or battery, load flexibility is required. We find that forecast-based methods for this power management handle tradeoffs in performance (e.g., cumulative power consumption, reducing intermittencies and meeting scheduled load targets) more effectively than power management strategies that do not use forecasting. Overall, our results indicate that forecasting for our stand-alone renewable energy system is most impactful when some loads must be met on or within a defined schedule.