Energy Flexibility Research Articles

Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility

This paper investigates the use of deep multi-agent reinforcement learning (MARL) for the coordination of residential energy flexibility. Particularly, we focus on achieving cooperation between homes in a way that is fully privacy-preserving, scalable, and that allows for the management of distribution network voltage constraints. Previous work demonstrated that MARL-based distributed control can be achieved with no sharing of personal data required during execution. However, previous cooperative MARL-based approaches impose an ever greater training computational burden as the size of the system increases, limiting scalability. Moreover, they do not manage their impact on distribution network constraints. We therefore adopt a deep multi-agent actor–critic method that uses a centralised but factored critic to rehearse coordination ahead of execution, such that homes can successfully cooperate at scale, with only first-order growth in computational time as the system size increases. Training times are thus 34 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes. Moreover, experiments show that the cooperation of agents allows for a decrease of 47.2% in the likelihood of under-voltages. The results indicate that there is significant potential value for management of energy user bills, battery depreciation, and distribution network voltage management, with minimal information and communication infrastructure requirements, no interference with daily activities, and no sharing of personal data.

Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps

The definition, characterization and implementation of Positive Energy Districts is crucial in the path towards urban decarbonization and energy transition. However, several issues still must be addressed: the need for a clear and comprehensive definition, and the settlement of a consistent design approach for Positive Energy Districts. As emerged throughout the workshop held during the fourth edition of Smart and Sustainable Planning for Cities and Regions Conference (SSPCR 2022) in Bolzano (Italy), further critical points are also linked to the planning, modeling and assessment steps, besides sustainability aspects and stakeholders’ involvement. The “World Café” methodology adopted during the workshop allowed for simple—but also effective and flexible—group discussions focused on the detection of key PED characteristics, such as morphologic, socio-economic, demographic, technological, quality-of-life and feasibility factors. Four main work groups were defined in order to allow them to share, compare and discuss around five main PED-related topics: energy efficiency, energy flexibility, e-mobility, soft mobility, and low-carbon generation. Indeed, to properly deal with PED challenges and crucial aspects, it is necessary to combine and balance these technologies with enabler factors like financing instruments, social innovation and involvement, innovative governance and far-sighted policies. This paper proposes, in a structured form, the main outcomes of the co-creation approach developed during the workshop. The importance of implementing a holistic approach was highlighted: it requires a systematic and consistent integration of economic, environmental and social aspects directly connected to an interdisciplinary cross-sectorial collaboration between researchers, policymakers, industries, municipalities, and citizens. Furthermore, it was reaffirmed that, to make informed and reasoned decisions throughout an effective PED design and planning process, social, ecological, and cultural factors (besides merely technical aspects) play a crucial role. Thanks to the valuable insights and recommendations gathered from the workshop participants, a conscious awareness of key issues in PED design and implementation emerged, and the fundamental role of stakeholders in the PED development path was confirmed.

Economy and energy flexibility optimization of the photovoltaic heat pump system with thermal energy storage

Photovoltaic systems have been widely applied in building energy systems. However, with the sharp increase in demand of electricity in buildings during heating and cooling seasons, the uncertainty of photovoltaic generation further exacerbates the peak-valley difference in electricity use. To improve the economy and energy flexibility of buildings in hot summer and cold winter zones of China, a rule-based operation strategy was proposed for the photovoltaic heat pump with thermal energy storage system to optimize the size of the thermal energy storage system and system operation, with aims of minimizing the total annual cost of the system and maximizing the self-consumption rate of the photovoltaic generation. The effects of grid export power limits, grid import power limits, feed-in tariffs, and PV generation on the system operation optimization results were also investigated. The results indicated that by integrating the thermal energy storage system into the photovoltaic heat pump system, the self-consumption rate of the photovoltaic generation was reduced by 2.39 %, the total annual cost of the system was decreased by 6.61 %, and the payback period of the thermal energy storage system was 1.31 years. The optimum size of the thermal energy storage system and the self-consumption rate of the photovoltaic generation decreased with the increasing grid export power limits and grid import power limits. In contrast, higher grid export power limits, grid import power limits, feed-in tariffs, and PV generation all resulted in a lower total annual cost. This study provided a systematic design and operation optimization method for building energy systems.

A real options-based framework for multi-generation liquid air energy storage investment decision under multiple uncertainties and policy incentives

Liquid Air Energy Storage (LAES) is a promising energy storage technology renowned for its advantages such as geographical flexibility and high energy density. Comprehensively assessing LAES investment value and timing remains challenging due to uncertainties in technology costs and market conditions. This study introduces a real options-based framework to evaluate investments in multi-generation LAES systems in China's power market by considering uncertain factors, such as investment cost and electricity price. Additionally, the impact of various incentives, including subsidies and preferential taxation policies, on LAES investment is investigated. The proposed investment decision framework is applied in a case study of a 10 MW-class multi-generation LAES system. Results show that the current economic environment is insufficient to trigger immediate LAES investment. The recommended optimal investment times are 2029 for Guangdong, 2031 for Jiangsu, and 2036 for Beijing and Qinghai without incentives. The levelized cost of storage at the optimal investment time is 0.105-0.174$/kWhe, and the optimal investment value is 882-9269k$. It is also found that preferential taxation policies can increase the LAES investment value. Increasing investment subsidies and technology learning effects have limited impacts on stimulating investments. In contrast, discharge subsidies are more favorable for promoting the deployment of LAES systems.

PCM as an energy flexibility asset: How design and operation can be optimized for heating in residential buildings?

In cold climates, residential buildings are often well-insulated to reduce heating costs but may lead to overheating. Incorporating Phase Change Material (PCM) into building envelope can serve as an effective measure to mitigate overheating and reduce cooling costs. However, the inappropriate incorporation of PCM in well-insulated building envelope might negatively affect the heating energy flexibility. Therefore, this paper explores the effectiveness of PCM in reducing heating costs by optimizing the PCM design and heating operation. The main objectives are to (i) quantify the performance of PCM in terms of overheating and cost savings, aligned with the recently introduced Time-of-Use (ToU) tariff and Spot pricing, (ii) identify the optimal design of PCM-incorporated building envelope, and (iii) apply a smart control with resetting temperature setpoints in-line with EN-ISO 52120-1 Standard. A multi-stage optimization process has been designed. It first identifies the optimal properties (i.e., melting temperature), position, and thickness of PCM. Secondly, it optimizes the profile of temperature setpoints, considering the seasonal thermal behavior of PCM. Co-simulation platform between IDA ICE 5.0 and Python 3.8 optimization engine is created for the heating control optimization. Results showed that PCM is more effective when incorporated into the interiors of external walls rather than in roofs. The optimal PCM design for Nordic buildings, featuring 75 mm-thick PCM with a melting temperature of 21 °C positioned within walls’ interior, has a varied impact on monthly energy savings across seasons, with a negative effect in spring and a significant positive effect in autumn. After the multi-stage optimization, the optimal scenario enables to achieve a maximum annual cost savings of 6.5 % with ToU tariff and 21.0 % with Spot pricing, while also eliminating overheating.

A probabilistic model for real-time quantification of building energy flexibility

Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.

Thermo-Chemical Characterization of Organic Phase Change Materials (PCMs) Obtained from Lost Wax Casting Industry

The high global energy demand drives the search for sustainable alternatives for energy production and storage. Among the most effective solutions are phase change materials (PCMs). In particular, organic PCMs offer a high capacity to store and release thermal energy in response to external thermal variations, even over a wide temperature range. They find profitable applications in various sectors, from construction to electronics, offering flexibility and considerable energy storage according to need. In the search for new and effective PCMs, reusing by-products from different industries would offer both economic and environmental benefits. With this goal in mind, several organic PCMs with different characteristics and origins were analyzed in the present study. Two of them were by-products of the lost wax casting industry. In fact, we wanted to verify whether this waste could be employed as an effective, low-cost PCM. For comparison purposes, two commercial PCMs were selected, namely a paraffin and a microencapsulated PCM. Finally, a PCM blend was produced by mixing a commercial PCM and a waxy by-product. The five selected or developed PCMs were subjected to different tests to investigate their chemical composition, thermal characteristics, and thermal stability before and after repeated (i.e., 100) cycles of melting and crystallization processes. The results demonstrated that the durability of the non-commercial PCMs with regard to thermal loads was not inferior, and was in some cases even superior, to commercial PCMs. This study therefore proposes an innovative path to reuse the by-products of different production processes to support the environment.

Watt’s Next? Leveraging Process Flexibility for Power Cost Optimization

The transition from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent power supply and demand. Although demand-side energy flexibility, offered particularly by industrial companies, is seen as a promising and necessary approach to address these challenges and realize benefits for companies, its implementation is not yet common practice. Often facing highly complex process landscapes and operational systems, process mining provides significant potential to increase transparency of actual process flows and to discover or reflect existing dependencies and interrelationships of activities, instances or resources. It facilitates the implementation of energy flexibility measures and enables the realization of monetary benefits associated with flexible process operation. This paper contributes to the successful integration of energy flexibility into process operations by presenting a design science research artifact called PM4Flex. This is a prescriptive process monitoring approach that uses linear programming to generate recommendations for pending process flows optimized under fluctuating power prices by utilizing established energy flexibility measures. Thereby, event logs and corresponding company- as well as process-specific constraints are considered. PM4Flex is demonstrated and evaluated based on its implementation as a software prototype, its application to exemplary data from two real-world processes exhibiting power cost savings of up to 75% compared to the original execution, and based on semi-structured expert interviews. PM4Flex provides new design knowledge at the interface of prescriptive process monitoring and the energy domain providing decision support to optimize industrial energy procurement costs.

Percolative Metal Microweb-Based Flexible Lithium-Ion Battery with Fast Charging and High Energy Density.

Flexible and high-performance lithium-ion batteries (LIBs) encounter challenges due to the inherent trade-offs in conventional electrode designs, particularly concerning mechanical flexibility and high energy density. Here, a novel percolative metal microweb-based electrode, fabricated via electrohydrodynamic processes, yielding a three-dimensional (3D) network structure with exceptional electrical properties and mechanical durability is introduced. This lightweight electrode design addresses the need for improved energy density by reducing the weight of the current collector, which typically accounts for more than 10% of the battery's weight. The 3D architecture of the electrode enhances the contact area with the electrolyte, thereby minimizing polarization voltage and improving lithium-ion diffusion. Furthermore, the low coating thickness-to-weight ratio promotes electron transfer and mitigates capacity fade at high current densities. The microweb-based full cell exhibits energy and power densities of 110 and 1,048Wkg⁻¹ at 10 C, respectively, which are the highest reported values among LIBs utilizing similar materials. The design of the electrode proposed in this study is expected to significantly contribute to the development of wearable and flexible electronics.

Day-ahead optimal dispatching for airport integrated energy system based on load-storage coordination

Airports are characterized by high energy consumption, with the cooling supply load being the primary contributor. Fully exploiting the optimization potential by utilizing the elastic characteristics of cooling supply load is the key to energy saving and decarbonization of airports. Firstly, this paper proposes to build a load-storage integrated energy system architecture by incorporating the ice storage system; secondly, a day-ahead optimal dispatching model with the goal of minimizing operating costs and carbon emissions is established; finally, taking an airport as an example, the NSGA-II algorithm is applied to solving the model, and various typical scenarios are compared and analysed. The daily photovoltaic consumption rate and indoor temperature range are used to evaluate the dispatching results. The system optimal dispatching model improves its performance greatly compared to the design condition, with a reduction of 33.3% for the operating cost and 31.9% for the carbon emissions. Five scenarios are included in the optimization project to characterize different environmental conditions, and the results demonstrate that the proposed optimization model can almost achieve full photovoltaic consumption while ensuring that the indoor temperature is in the passengers' comfort zone in the face of different degrees of photovoltaic output fluctuations and outdoor temperatures. Research outcomes in this paper provide a reference for improving the energy flexibility potential of airports.

Flexibility enhancement of urban energy systems through coordinated space heating aggregation of numerous buildings

Due to the intrinsic flexibility of thermal energy in buildings, collective space heating can potentially offer significant benefits to urban energy systems. In this context, this study introduces a novel thermal energy normalisation approach to capturing the collective indoor temperature dynamics of clustered buildings. The feasible operational region of the normalised equivalent building thermal energy model is derived from individual building parameters and temperature requirements through the constraint-tightening technique. By employing the thermal energy normalisation approach, the aggregate flexibility of building populations is quantified and integrated into the energy management optimisation problem for the urban energy system operator. To associate the response of the normalised equivalent building model and individual building models, a deadbeat controller is designed to synchronise the energy levels and realise robust operation for buildings. Through a series of case studies, the simulation results demonstrate that the proposed preheating coordination strategy can facilitate energy savings for energy system operation, cost-effectively perform congestion management to ensure no local network is overloaded, and ensure the satisfaction of indoor temperature requirements.

Intermittent demand-controlled ventilation for energy flexibility and indoor air quality

Buildings play a crucial role in ensuring power grid stability by providing energy flexibility. Existing research mainly improves the building energy flexibility through energy storage, but requires additional equipment costs. This study proposes a new source of building energy flexibility by properly managing indoor air quality with intermittent demand-controlled ventilation (iDCV), which requires no additional equipment investment. This study reveals the mechanisms of enhancing energy flexibility and develops a nonlinear programming algorithm to optimize the dynamic operation of the iDCV. The robustness of the proposed iDCV in enhancing energy flexibility is verified under different application scenarios in different climate zones. The results show that compared with the existing method, the proposed iDCV enhances the energy flexibility with a peak load reduction of 17.1 %–36.1 % under the scenario of constant electricity price and with a flexibility factor enhancement of 9.5 %–80.8 % under the scenario of time-of-use electricity price. Besides the benefit to the power grid with the enhanced energy flexibility, the proposed iDCV also delivers benefits to building users with energy savings of 19.6 %–22.0 % and cost savings of 21.5 %–26.1 %. Therefore, the proposed iDCV provides dual benefits for both the power grid and building users and contributes to developing energy-flexible, low-carbon, and healthy buildings.

Model predictive controls for residential buildings with heat pumps: Experimentally validated archetypes to simplify the large-scale application

Exploiting the energy flexibility resulting from thermal loads management in buildings is one of the most promising solutions to contribute to the energy transition targets. To offer energy flexibility to the grid, the building should meet the requirements: (i) it needs electrically powered generation (e.g., through Heat Pumps) and (ii) advanced control techniques (e.g., Model Predictive Controls) must be implemented. In recent years, the scientific community has produced many studies demonstrating the potential of Model Predictive Controls combined with Heat Pumps to exploit energy flexibility in buildings. However, a large-scale deployment of such control techniques is still far off, both because of the not yet widespread use of Heat Pumps and the computational challenges involved in implementing them. The aim of this study is to contribute to the deployment of advanced control techniques for Heat Pumps systems in buildings by simplifying their implementation. At this aim, validated archetypes of Model Predictive Controls for Heat Pumps in residential buildings are proposed. The availability of archetypes can greatly facilitate the practical application of Model Predictive Control. In fact, they are adaptable to the characteristics of different buildings and Heat Pump installations and their structure is designed to have an acceptable trade-off between calculation time and prediction reliability. The archetypes cover three different types of heating systems: low temperature radiators without (i) and with (ii) integrated heat storage devices and (iii) underfloor heating system. All archetypes are applied to a real Heat Pump and validated through experimental campaigns. During the experimental test all the archetypes proved to be effective in controlling the real system and the results showed good reliability of the prediction model in the control (for all archetypes a Root Mean Square Error lower than 0.44 °C was obtained) and an optimizer success rate in Model Predictive Controls greater than 91 %. The archetypes proposed are provided as open-source tools that can be reused for similar cases to facilitate Model Predictive Controls implementation in heat pump systems.

The political economy of electricity market coupling: Comparing experiences from Europe and the United States

Advancing the integration of neighbouring power markets is widely identified in the literature as a key enabler of power system efficiency, flexibility, and variable renewable energy integration. Nonetheless, the existing economic literature offers only partial explanations of why greater market integration is difficult to achieve in the real world despite its evident advantages in terms of overall welfare, supply security, and competitiveness. It is argued that power market coupling is an inherently political process involving significant institutional contestation and adaptation, and hence requires a more nuanced political analysis to be fully understood. This paper adds to the existing literature by conducting a comparative political economy analysis of electricity market coupling processes in Britain, Italy, and California, spanning from 2013 to 2021. It seeks to unpack how differences in the political economy contexts of these jurisdictions influenced the electricity market coupling process with their respective neighbouring systems. The analysis draws on 86 key policy documents, and 53 in-depth interviews with senior power system stakeholders in the three jurisdictions. Results widely align with claims in the political economy literature that market coupling outcomes do not simply reflect the most efficient solution. While all three jurisdictions have shown a commitment to enhance regional integration of short-term wholesale energy markets, there is considerable variation in policy outcomes, particularly the extent to which different segments of the market have been coupled. These differences can largely be attributed to variations in the political economy contexts of the three jurisdictions, including multi-level governance structures, diplomatic relations with neighbouring countries, and interactions with national political priorities and contexts. The main implications for the governance of electricity market coupling are discussed in the conclusion.

Analyzing the flexibility potential of bus fleet operators in Germany

The transition to smart energy systems is a crucial component for ensuring sustainability and reducing carbon emissions. Electrification is a key factor in achieving these goals, with the transport sector being an integral part of the equation. The integration of the transport sector with the electricity sector will facilitate a reduction in carbon emissions. This paper assesses the potential of electric bus depots to function as smart energy infrastructures. Analyzing the energetic system flexibility of the electrified public transport system is at the core. Previous studies emphasize the importance of identifying and managing the optimal operation strategies of electrified transport to achieve system flexibility. This work concentrates on Germany as a reference market for balancing and electricity markets at the center of the EU. The flexibility potential of a bus fleet with 80 electric buses is analyzed under optimal participation in the short-term electricity and balancing market. The bus fleet operator acts as a storage systems aggregator, which combines mobile and stationary storages to enhance energy flexibility. The study measures the potential contribution for the stability of the electricity grid in Germany. The additional battery degradation that arises with the provision of balancing services is part of the economic equation. The analysis is based on historical data from 2020, 2021, and 2022 and investigates hypothetically lower and higher demand for balancing energy in the load-frequency control area of Germany and Denmark. The paper concludes by demonstrating the feasibility of the electrified bus depot as an integral component of smart energy systems. These findings contribute to a better understanding of the electrification of transport, sector integration, and the role of infrastructures in achieving smart energy systems and showcases the attractiveness of this business model.

Residential consumer enrollment in demand response: An agent based approach

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ±10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

Integrated demand response method for heating multiple rooms based on fuzzy logic considering dynamic price

We consider an educational building heated by a combination of district heating (DH) and a local air source heat pump. We have developed an integrated demand response method for multiple rooms, consisting of an optimization layer and a control layer, to maintain thermal comfort and save energy and costs. For the optimization layer, we apply fuzzy logic to adjust indoor temperature setpoints to respond to dynamic heat prices and propose an optimal heat supply method to find optimal heat supply schemes. For the control layer, a multi-objective model predictive control (MPC) has been developed to manage indoor thermal conditions across multiple rooms. To test and verify the integrated demand response method, we build a multi-room simulation model using the CARNOT Toolbox. The results show that adopting different indoor temperature setpoints during working and nonworking hours, combined with the MPC method, has an energy-saving potential of 9.1 % compared to maintaining a constant indoor temperature using DH alone. Adjusting temperature setpoints using fuzzy logic utilizes the building's heat storage capacity to increase energy flexibility, reaching 16.0 % savings in energy and reducing 12.6 % heating costs.

Aggregator-supported strategy for electric bus fleet charging: A hierarchical optimisation approach

The increasing adoption of electric buses in cities presents an opportunity for these vehicles to serve as mobile energy storage systems, providing valuable grid services through their substantial battery capacity. However, the complex interplay of fleet management and energy market participation requires that public transportation operators (PTOs) use adequate optimisation models and algorithms to make the best operational decisions. To address these issues, our research proposes an aggregator-supported charging strategy using a bi-level optimisation framework for electric bus fleets. The main goal is to achieve a balance between reducing PTO operational costs and maximising aggregator revenues, while ensuring reliable bus system operation. The results indicate that a coordinated charging strategy, including energy arbitrage and reserve provision, can significantly reduce operational costs, although potential limitations arise from associated battery degradation costs. Furthermore, managing fleets for reserve services appears to be more lucrative than engaging in energy arbitrage schemes. In this context, this study emphasizes the role of aggregator entities in managing the energy flexibility of electric bus fleets and enhancing the sustainability and cost-effectiveness of urban public transportation.

Long-haul truck charging planning problem considering time flexibility and energy flexibility

The study explores opportunity charging as a solution for extending electric truck range during routine stops. It proposes a mixed-integer optimization model that combines opportunity charging with truck charging trip planning to optimize charging time and energy. The model examines variability in charging strategy and integrates potential driver choices and behaviors, including hub-charging and en-route charging options. An improved heuristic method is proposed, and the model's effectiveness is verified through numerical experiments. We present the Improved Dung Beetle Optimization (IDBO) algorithm based on the sine-cosine transformation process to solve the model. The proposed IDBO algorithm has the best optimization capability compared to typical genetic algorithms and traditional Dung Beetle Optimization (DBO), and the resulting total cost value was the lowest in all three cases used for the calculation.

A two-stage distributed stochastic planning method for source-grid-load-storage flexibility resources considering flexible ramp capacity

With rapidly increasing levels of renewable energy penetration, flexibility resources play an ever more critical role in the future power system. This paper describes a two-stage stochastic mixed integer second order cone (MISOC) method for flexibility resource planning (VFRP) considering source-grid-load-storage interactions. Considering renewable energy penetration levels and flexibility resource characteristics, this study quantifies the extent to which the share of renewable energy generation affects cost performance, and assesses the priority and potential of investing in flexibility resources. Moreover, the multi-type flexibility resource portfolio allocation method and energy storage only approach are compared in terms of socio-economic benefits. A multi-cut Benders-based approach is used for decomposing large-scale stochastic expansion planning problems to reduce the computational burden. Numerical simulation tests on the modified energy test systems I39G20 and F213G20 verified the correctness and effectiveness of the proposed flexible resource planning models and solution algorithms. The simulation results show that retrofit of coal-fired units for better flexibility has a higher investment return. Still, energy storage presents a tremendous investment prospect and potential. Finally, the multi-type flexibility resource portfolio allocation method is more cost-effective than an energy storage only approach.