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Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications. [ABSTRACT FROM AUTHOR]

Energy management models for buildings have been designed primarily to reduce energy costs and improve efficiency. However, the focus has recently shifted to GEBs with a view toward balancing energy supply and demand while enhancing system flexibility and responsiveness. This paper provides a comprehensive comparative analysis of GEBs and other building energy management models, categorizing their features into internal and external dimensions. This review highlights the evolution of building models, including intelligent buildings, smart buildings, green buildings, and zero-energy buildings, and introduces eight distinct features of GEBs related to their efficient, connected, smart, and flexible aspects. The analysis is based on an extensive literature review and a detailed comparison of building models across the aforementioned features. GEBs prioritize interaction with the power grid, which distinguishes them from traditional models focusing on internal efficiency and occupant comfort. This paper also discusses the technological components and research trends associated with GEBs, providing insights into their development and potential evolution in the context of sustainable and efficient building design. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs), as facilitators of grid stability and flexibility, provide a critical solution to the energy infrastructure's evolving demands, underscored by the growing integration of renewable energy sources (RES) and the rapid increase in EV adoption worldwide. This trend is particularly evident in Europe which is experiencing dramatic increases in both the adoption of RES and EVs. Vehicle‐to‐grid (V2G) technology allows EVs to operate as a two‐way power flow to both draw and feed electricity into the grid. This multidisciplinary overview examines the role of V2G systems in enhancing grid performance, identifying corporate vehicle fleets as key flexibility providers, and integration with Smart Grid technologies as a key element for successful V2G implementation. In a scoping analysis of recent literature (2005–2024), we identify challenges such as privacy, security, and regulatory compliance as well as a critical gap in establishing economically sustainable models for aggregators, distribution system operators (DSOs), generation companies (GENCOs), and end‐users. Drawing from these insights, we then discuss the necessity for future research to develop models that ensure equitable benefits across stakeholders and the importance of models that can adapt to country‐specific mechanisms. The findings from our overview argue that the integration of EVs, V2G, and RES are essential components for developing future energy systems that are resilient, adaptable, decarbonized, and sustainable. This article is categorized under:Cities and Transportation > Electric MobilityEnergy and Power Systems > Energy InfrastructureEnergy and Power Systems > Energy Management [ABSTRACT FROM AUTHOR]

This paper presents a case study of energy flexibility strategies for a building located in the hot climate of Dubai, a type of climate in which energy flexibility has been under-researched. Energy flexibility is changing the routine-consumption profile and deviating from the normal operation of the building by the users to manage the variability in the load profile and cost of electricity. The three flexibility strategies being considered are based on the modulation of the indoor air temperature setpoint profile while considering different marginal costs for electricity. The main objective is to quantify the energy storage flexibility of each strategy and evaluate its impact on the system ramping and load factor. The study was carried out utilizing a grey-box, resistance–capacitance model of the building, which was validated against experimental measurements. This study is the first to use the following five indicators simultaneously: load factor, system ramping, storage capacity, peak-period demand reduction, and cost savings. Combining these indicators helps building facility managers and distribution system operators (DSOs) better understand the implications of implementing a specific flexibility strategy in a building or a group of buildings. When comparing the indicators of each strategy with each other, it was observed that depending on the amplitude of the change in the electricity cost signal during the peak period, a significant cost reduction of more than 25% could be achieved through the implementation of specific flexibility strategies compared with the normal baseline operating condition. [ABSTRACT FROM AUTHOR]

This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.

Building energy flexibility is an important resource for a sustainable and resilient power grid, and an important measure to reduce utility costs for building owners. Quantifying energy flexibility for existing buildings can provide critical insights in optimizing their operation. Data-driven methods for building energy modeling and analytics are gaining popularity due to the increasingly available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. However, their application in quantifying the energy flexibility of real buildings is still limited due to the heterogeneous data types and limited data availability. This study proposes a framework for building-level data-driven energy flexibility quantification that considers different levels of data availability and use cases. Two case studies with real building data collected at different scales were conducted to demonstrate the proposed framework for different purposes.

Geothermal energy is a unique energy source in the energy policy mix that would help the clean energy transition and energy independence, supporting the energy needs in heating and electricity. Although there have been studies on the opportunities and challenges of renewable energy, this paper is the first paper that concentrates on geothermal energy for three distinct countries, Italy, Belgium, and Iceland, for the first time. Using semi-structured interviews that will cover the stakeholders representing the quadruple helix (academia, citizens, policymakers, and industry), this paper aims to find the unique and common opportunities and barriers the geothermal sector has. Shared challenges include financial barriers, regulatory complexities, environmental issues, and the need for improvement in the social acceptability of geothermal energy. Despite these challenges, geothermal energy, a promising energy source for clean transition, could create opportunities like improved household welfare through combined uses in district heating and electricity and have the potential to generate employment opportunities. [ABSTRACT FROM AUTHOR]

In the face of substantial environmental impact, energy-intensive industries are compelled to deal with a spectrum of future energy scenarios, including the adoption of electrification and hydrogen use. In addition, these sectors are under pressure to enhance energy efficiency and adaptability to reduce costs. The integrated planning and control of production and energy system components is challenging due to the complexity and lack of available methods, though. This paper addresses this gap, by introducing a simulation-based method that optimizes production and energy system components. The planning method is based on a multicriteria meta-heuristic with an integrated simulation of production and energy system behavior. It evauates different future energy scenarios with electrification and hydrogen use including variable energy markets and storage systems. The method is applied and validated in a case study in an energy-intensive steel production. The results show the significant benefits of the dynamic planning and optimization for various variables in two different heat-treatment process technologies. [ABSTRACT FROM AUTHOR]

According to the European Energy Efficiency Directive for Buildings, member states are required to develop long-term strategies to adopt more sustainable, secure, and decarbonized energy systems in buildings by 2050. In this line of approach, an optional common regime has been established to define and calculate the smart readiness of buildings and assess their ability to adapt their operation to the needs of the occupants and the network. Thus, the smart readiness indicator (SRI) emerged, which assesses technological readiness by examining the presence and evaluation of the functionality level of various smart services, aiming at energy savings, the ability of the building to respond to users' needs, and energy flexibility. This paper focuses on examining the SRI calculation methodology's application to an office building, which is currently being deeply renovated. Initially, there is an analysis of the SRI, its calculation methodology, and its goals. This is followed by the practical calculation part of the SRI for a typical office building located in Greece and belonging to the climate zone of southern Europe. The results indicate that the SRIs application is not a straightforward issue since parameters that need to be considered are not regulated to the same degree. On the other hand, SRI can provide a stimulus for exploiting the renovation potential of buildings, precisely by integrating the various aspects and linking those to the use of innovative technologies. [ABSTRACT FROM AUTHOR]

In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits. [ABSTRACT FROM AUTHOR]

This study presents a novel methodology for evaluating the flexibility of residential electricity consumers under diverse operational conditions introducing innovative flexibility indicators. The increasing demand for energy flexibility from the demand side is crucial for ensuring the power grid’s economic stability and resilience. Buildings offer a range of potential flexibility sources that can be effectively leveraged for this purpose. Accordingly, this paper formulates energy flow management in a Smart House using a Mixed-Integer Linear Programming (MILP) framework, incorporating the scheduling of shiftable appliances and an Energy Storage System (ESS). By defining different consumption strategies, the feasible region for Energy-Time Profiles (ETPs) is explored through the establishment of the corresponding optimization problems. A series of case studies are presented to evaluate the impact of operational constraints on the feasible region. Finally, by meticulously analyzing the characteristics of ETPs extracted from these case studies, the proposed flexibility indicators are introduced, quantifying the Smart House’s energy flexibility potential. Furthermore, a sensitivity analysis is conducted to scrutinize the influence of key parameters on the new flexibility indicator, encompassing ESS characteristics. The findings substantiate the efficacy of the proposed indicators in providing comprehensive insights into the potential energy flexibility under a range of operational conditions.

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings.

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

Energy flexibility aggregation is an emerging concept regarded as a potential solution to the challenges of integrating distributed renewable energy sources. For aggregators to make informed decisions on how to utilise the energy flexibility of prosumers in their portfolios, it is crucial for them to use a suitable quantification method. This paper proposes a novel quantification method for aggregated energy flexibility based on flexibility curves created from the relationship between flexible power and its sustained duration. The proposed method also considers the asymmetric and non-linear properties of energy flexibility. The flexibility curves provide valuable insights for aggregators to make informed decisions on how to utilise their portfolios in a more optimal manner. We describe the quantification method with a step-by-step process. A simulation-based case study is conducted to illustrate the quantification process of residential heating systems. The aggregated flexibility curves and the rebound effects of 1000 different heat-pump-based buildings are constructed. The building’s thermal behaviour is modelled using a resistive-capacitive model. We found that the power at which the flexibility can be activated highly depends on the activation duration. Additionally, the rebound effect can be quite substantial, with around 1.8 times the energy of the activation itself. We also noted an interesting phenomenon of rebound overshooting as the system oscillates to a stable position.

The increasing share of renewable energy generation poses a challenge to maintaining the adequacy of power generation and demand. Planning the operation of flexible energy resources helps stabilize this balance. Optimization models are needed for operation planning. The use of a predefined and validated optimization model avoids modeling errors, but parameterization is still necessary and error-prone. This work presents a methodology for determining parameters of optimization models for various kinds of flexible energy resources. The parameters are derived from time series data of the resource operation after preprocessing. This methodology includes algorithms for determining operational boundaries, the input–output relationship, system states, and parameters for storage systems. Connections of flows between individual resources within a system are extracted from a standardized information model. A case study of a combined heat and power system demonstrates the applicability of the methodology by deriving a set of parameters from time series data and an information model of the system's structure. The model parameterized by means of the methodology shows very good alignment with a validation time series data set with a normalized root mean square error of 1% (generator), respectively, of 6% (heat exchanger).

This paper proposes obtaining maximum and minimum daily cumulative energy curves and introduces novel hourly and daily energy flexibility indices. Also, it develops a generic methodology that quantifies and formulates energy flexibility as the possible power increase (P_in) or decrease (P_dec) within operational limits. The proposed method can be applied to derive maximum and minimum energy flexibility curves for different devices and aggregate them to extract hourly or daily energy flexibility indices based on the calculation area between daily cumulative energy curves in an hour and 24 hours. The proposed energy flexibility estimation is evaluated by doing offline digital time-domain simulations on a 100-bus home-residential active distribution network (ADN), including flexible equipment/devices (e.g., washing machines, dishwashers, domestic heat water, battery, photovoltaic (PV) panels, and plug-in hybrid electric vehicle (PHEV) charging stations) in MATLAB/Simulink software environment. Then, a price-sensitive model of every flexible equipment is introduced, and ultimately, the effect of electricity price changes on energy flexibility is evaluated. The simulations and comparisons of the energy flexibility potential of different pricing scenarios effectively prove the proposed strategy’s effectiveness, accuracy, and authenticity.

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

This paper presents a case study of energy flexibility strategies for a building located in the hot climate of Dubai, a type of climate in which energy flexibility has been under-researched. Energy flexibility is changing the routine-consumption profile and deviating from the normal operation of the building by the users to manage the variability in the load profile and cost of electricity. The three flexibility strategies being considered are based on the modulation of the indoor air temperature setpoint profile while considering different marginal costs for electricity. The main objective is to quantify the energy storage flexibility of each strategy and evaluate its impact on the system ramping and load factor. The study was carried out utilizing a grey-box, resistance–capacitance model of the building, which was validated against experimental measurements. This study is the first to use the following five indicators simultaneously: load factor, system ramping, storage capacity, peak-period demand reduction, and cost savings. Combining these indicators helps building facility managers and distribution system operators (DSOs) better understand the implications of implementing a specific flexibility strategy in a building or a group of buildings. When comparing the indicators of each strategy with each other, it was observed that depending on the amplitude of the change in the electricity cost signal during the peak period, a significant cost reduction of more than 25% could be achieved through the implementation of specific flexibility strategies compared with the normal baseline operating condition.

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management.

Compensation structures for residential solar are evolving toward a model that incentivizes using battery storage to maximize solar self-consumption. Using metered data from 1,800 residential customers across six U.S. utilities, we show that batteries operated solely in this manner provide customer bill savings up to $20-30 per kWh of storage capacity annually, but virtually no grid value. Relative to market-based dispatch, this value gap remains across customers and will become more severe over time, insofar as increased renewable energy penetration leads to more volatile wholesale prices. This inefficiency primarily stems from residential batteries largely sitting idle on peak days. We show that incentivizing storage customers to respond to market prices, particularly on peak days, would enhance both private and public value. Unconstrained grid discharging increases exports to distribution networks, but 50-70% of the potential market value could be achieved without materially degrading solar self-consumption levels or increasing local grid stress.

The positive energy district (PED) is a rather recent concept that aims to contribute to the main objectives of the Energy Union strategy. It is based on an integrated multi-sectoral approach in response to Europe's most complex challenges. But to what extent have its development and implementation been supported by research and innovation programs? And what is the state of the art of its implementation and effective penetration into the current energy systems of European cities, according to the evidence provided by the scientific literature? This study aims to investigate these issues, providing a critical overview of the PED situation by means of a systematic literature review based on the use of open-access bibliometric software supplemented with content analysis. The results show that less than half of the documents analyzed refer to actual case studies, 80% of which were funded as part of research projects. This seems to lead to the conclusion that although PEDs have been strongly encouraged by the scientific community and policy initiatives at the European level, their implementation in cities is still limited. Moreover, an uneven distribution among countries can be observed. To overcome the existing barriers to PED diffusion and implementation, it would be useful to provide more ad hoc funding and, above all, facilitate its accessibility also by municipalities not yet well integrated into European projects, initiatives, and networks. [ABSTRACT FROM AUTHOR]

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP. [ABSTRACT FROM AUTHOR]

Thermal processes represent a significant fraction of industrial energy consumptions, and they rely mainly on fossil fuels. Thanks to technological innovation, highly efficient devices such as high-temperature heat pumps are becoming a promising solution for the electrification of industrial heat. These technologies allow for recovering waste heat sources and upgrading them at temperatures up to 200 °C. Moreover, the coupling of these devices with thermal storage units can unlock the flexibility potential deriving from the industrial sector electrification by means of Demand-Side Management strategies. The aim of this paper is to quantify the impact on the energy system due to the integration of industrial high-temperature heat pumps and thermal storage units by means of a detailed demand–supply model. To do that, the industrial heat demand is investigated through a set of thermal process archetypes. High-temperature heat pumps and thermal storage units for industrial use are included in the open-source unit commitment and optimal dispatch model Dispa-SET used for the representation of the energy system. The case study analyzed is Belgium, and the analysis is performed for different renewable penetration scenarios in 2040 and 2050. The results demonstrate the importance of a proper sizing of the heat pump and thermal storage capacity. Furthermore, it is obtained that the electrification of the thermal demand of industrial processes improves the environmental impact (84% reduction in CO2 emissions), but the positive effect of the energy flexibility provided by the heat pumps is appreciated only in the presence of a very high penetration of renewable energy sources. [ABSTRACT FROM AUTHOR]

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory. [ABSTRACT FROM AUTHOR]

Unexpected power outages and extreme weather encouraged research on energy-resilient buildings throughout the world. Resilient building research mainly focuses on hot weather rather than cold extremes. This study defines resilience terminologies based on the available literature and discusses the impact of energy efficiency on energy resilience performance in energy-efficient buildings due to abrupt power outages in an extremely cold climate. The assessment involves the case simulation of a multistory apartment located in southern Finland at design outdoor conditions (−26 °C) in IDA-ICE 4.8, a dynamic building simulation software, and its techno-economic assessment to ensure building resilience for up to 7 days of power outages. The assessment shows the efficient building envelope can enhance the time taken by the building to drop the indoor temperature to the threshold by approximately 15%. Additionally, the efficient heating system along with the building envelope can reduce the instantaneous power demand by up to 5.3 times, peak power demand by up to 3.5 times, and on average power consumption by 3.9 times. Similarly, the study finds that the total energy requirement during a blackout can be reduced by 4.1 times. The study concludes that enhanced building resilience is associated with energy-efficient parameters such as an efficient energy system and an efficient building envelope that has low thermal losses and high thermal inertia retention. The batteries contribute the maximum proportion to the overall retrofitting cost, and the proportion can go up to 70% in baseline configurations and 77% in efficient configurations of buildings. The analysis concludes that the required investment varies largely with the technologies involved and the combination of components of these energy systems. The assessment finds that the high investment costs associated with batteries and battery recharging costs are the main bottlenecks to feasible flexibility in market participation. [ABSTRACT FROM AUTHOR]

Capacities of residential photovoltaics (PV) and battery storage are rapidly growing, while their lifecycle cost and carbon implications are not well understood. Here, we integrate PV generation and load data for households in California to assess the current and future lifecycle cost and carbon emissions of solar-plus-storage systems. Our results show that installing PV reduces $180-$730 and 110-570 kgCO2 per year per household in 2020. However, compared to solar-only system, adding battery storage increases lifecycle costs by 39%-67%, while impact on emissions is mixed (-20% to 24%) depending on tariff structure and marginal emission factors. In 2040, under current decarbonization and cost trajectories, solar-plus-storage leads to up to 31% higher lifecycle costs and up to 32% higher emissions than solar-only systems. Designing a tariff structure with wider rate spreads aligned with marginal carbon emissions, and reducing the costs and embodied emissions of batteries are crucial for broader adoption of low-carbon residential solar-plus-storage.

The energy-only-market implemented in China cannot strongly support large-scale renewable energy expansion because the renewable energy expansion may disorderly phase out non-renewable power capacity. However, non-renewable power capacity, particularly the coal-fired power capacity in China, can provide vital power system adequacy needed by renewable energy expansion. We introduce capacity payments to orderly retire current coal-fired power capacity by transforming some of it into reserve capacity in order to support renewable energy expansion. Using generation and transmission expansion results from the SWITCH-China model, this paper proposes an orderly retirement path based on the assumption of implementing capacity payments. Our results show that roughly 100-200 gigawatts (GW) of coal-fired power capacity can continue to serve through 2050, and most of it is used as reserve capacity. Capacity payments of 400-700 billion yuan are needed to achieve this retirement path, and a higher adequacy requirement needs higher payments.