Flexible Electricity Research Articles

Flexible electricity market clearing zones

Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid’s physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of-concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.

A privacy protection scheme to resist ring selection attack in peer-to-peer double auction V2V electricity transaction for charging stations

With the development of Electric Vehicles (EVs), the number of public charging piles cannot meet the immediate charging needs of EV users sometimes. The charging stations provide flexible electricity transaction schemes for users by managing charging piles and Vehicle-to-Vehicle (V2V). Users can freely choose transaction partners and submit transaction plans to multiple charging stations. The mobility of EVs results in users randomly selecting rings multiple times, which may cause them to appear in different rings. Attackers can link multiple transaction plans and infer user privacy information. The leakage of transaction information may threaten the fairness of transactions. Aiming at the above situations, a privacy protection scheme to resist ring selection attack in Peer-to-Peer double auction V2V electricity transaction for charging stations is proposed. Firstly, the ring signcryption algorithm is improved to resist ring selection attack. Secondly, a real identity verification algorithm is designed to track malicious users. Theoretical analysis proves that the security of the proposed privacy protection scheme. The elliptic curve point multiplication is used in the scheme to improve computational efficiency. Experiments show that this scheme has low computational and communication costs. Verification cost increases slowly with the increase of the number of ring members.

Development of low-carbon technologies in China's integrated hydrogen supply and power system

Hydrogen and electricity are crucial and interdependent energy carriers in China's pursuit of carbon neutrality, suggesting the necessity of utilizing cost-effective low-carbon technologies that facilitate their integrated development. The cost-optimal, provincial level, deployment of low-carbon technologies under this long-term goal remains to be determined. This study employs the REPO model to identify the cost-optimal, low-carbon hydrogen production mixes and the evolution of the integrated power system of China from 2020 to 2050. The integrated planning and operation of hydrogen supply and power systems are explored at the provincial level. The role of high-temperature gas-cooled reactors in this integrated energy system is also analyzed. The results reveal that electrolytic hydrogen would dominate China's hydrogen supply after 2040, with alkaline, proton exchange membrane, and solid oxide electrolyzers produce over 1 Mt of hydrogen in the short term, by 2035, and in 2050. Leveraging the low-carbon heat production of high-temperature gas-cooled reactors in addition to its electricity generation to meet the thermal requirements of solid oxide electrolyzers could boost the output to 4.2 Mt in 2050 and reducing the total system CO2 emissions and costs by 2.28% and 0.05%, respectively. By 2050, the integration of hydrogen supply and power systems also generates up to 2194 TW h of flexible electricity demand by electrolyzers, which raised the renewable energy penetration by 4 percentage points while decreasing the need of flexible natural gas power generations and energy storages. This study is valuable for proposing the analytical framework and performing the provincial-level study of decarbonization of China's integrated hydrogen supply and power system.

Shifting patterns – The patterned enactments of flexible electricity consumption by Norwegian households

Based on semi-structured interviews with 20 households, this study explores how households interpret their possibilities to time shift their electricity consumption. Drawing from the concept of flexibility capital (Powells and Fell, 2019), we illustrate the patterned nature of flexibility work. We complement this with an analysis of how people enact time shifting in micronetworks that include people, artifacts, infrastructure, knowledge, and institutions (Sørensen et al., 2000). We identify four types of micronetworks that show that households interpret and domesticate flexible consumption in a variety of ways. First of all, most households with high flexibility potential did not see their advanced energy technologies as useful for time shifting consumption and thought that time shifting is meant to be done by others in society. Meanwhile, many households with lower flexibility potential found time shifting consumption meaningful, interpreting it as rescheduling dishwashing and laundry. The findings imply that there is a need to account for whether households view the technologies they possess as useful for flexibility work; in other words, whether these technologies are understood and enacted as flexibility capital. The study discusses the possibility that many households with high flexibility potential may be indifferent to price incentives for time shifting. Additionally, the findings highlight that households are actively constructing the situated meaningfulness of time shifting. It is important that professional communities acknowledge the variety of interpretations of what time shifting is about instead of seeing these as just overflows of the dominant smart grid logic.

Decision process for industry 5.0

ABSTRACT Industry 5.0 is an emerging paradigm that enhances the established Industry 4.0. Automation, digitalisation, big data, and artificial intelligence are becoming complemented by human-centricity, active cooperation between people and machines, and decision-making based on human expertise. This research introduces a generic decision-making process for Industry 5.0. It defines the underlying principles to incorporate multi-criteria decision models, decision procedures, and decision support technology into business and production processes for Industry 5.0. We justify the motivation to aggregate holistic objectivedecisions, which are inferred automatically from big data, with subjective judgements and knowledge of human decision-makers to guarantee unbiased actions. We draw parallels with the aggregation-disaggregation paradigm and constructive learning to address the human-centric specification of preferences in cooperation with technology-driven entities. We assess the relevance and efficiency of decision support for Industry 5.0 with a SWOT analysis. Finally, we apply the introduced generic process to the use case of the flexible electricity supply market.

The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study

The transition of the transport sector to e-mobility poses various challenges but also provides great flexible load and supply potential and thus enables a stronger coupling of the transport sector with other sectors. If emerging opportunities such as bidirectional charging in the context of Vehicle-to-Home and Vehicle-to-Grid applications are utilised, a previously unimagined load management and storage potential can be tapped. This can transform e-mobility from an additional burden to the grid to a grid-supporting factor that enables greater integration of renewable energies and reduces additional investments in infrastructure like grid expansion and stationary storage systems. In order to investigate this potential, within this work we examine simulation based various Vehicle-to-Home (PV self-consumption, load shifting due to flexible electricity tariff) and Vehicle-to-Grid (secondary reserve) scenarios for different driving profiles for a residential building with heat pump, PV system and optionally a small wind turbine. In addition, a charge load optimisation is carried out using a genetic algorithm. The energy quantities, saving potential and additional number of battery cycles are quantified. The results show that, despite additional battery degradation, significant financial incentives can be achieved.

Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level

The energy transition based on variable renewable energy sources raises concerns on satisfying the electricity demand at all hours of a year, which is amplified by the ongoing energy system electrification. Balancing variable electricity supply and inelastic demand requires additional sources of flexibility in the system: flexible electricity generation, energy storage, grids, flexible demand, and overall sector coupling. Electrification of transport increases the electricity demand; however, it offers the opportunity to use additional low-cost flexibility from electric vehicles batteries. The LUT Energy System Transition Model was modified to model the smart charging and vehicle-to-grid functionality of electric vehicles in integrated energy systems. The results show that, in countries with a large fleet of electric vehicles, smart charging and vehicle-to-grid allow for a substantial reduction of energy storage requirements, reducing the electricity and heat storage capacity by 35% and 25%, respectively and leading to 4% lower system cost. Flexible operation of electrolysers required for e-fuels synthesis have a similar effect and, in combination with smart charging and vehicle-to-grid, can lead to 8% lower system cost. The impact of vehicle-to-grid naturally depends on assigned operational cost; however, it contributes to system cost reduction even at high costs of 50 or 100 €/MWh.

Multi-dimensional management of smart distribution networks: Comparative analysis of box and polyhedral methods for modeling uncertainties

By using advanced automation services, distribution systems have been transformed from passive networks to smart and active infrastructures. In this regard, due to the complex interaction of different entities, operators have faced multifaceted challenges in securely exploiting the electric power system. However, optimal planning and operation of smart distribution networks have made the reach of a more stable and flexible electricity network possible. To this end, this study proposes a new operational model of effectively integrating demand response and battery energy storage systems (BESS) to reduce the power exchange with the sub-distribution substation. To optimize the BESS charging and discharging cycles, the degradation limit is considered for its optimal operation in the electric system. Herein, locally controllable generators are taken into account to support reactive power as well as assist with the flattening of the voltage profile. This paper considers emission cost in the performance of local controllable generators (LCG), which adds a critical layer of optimization to distribution network management. This enables the system to balance grid stability, affordability, and environmental responsibility. Moreover, box and polyhedral methods have been investigated and compared for effective and uncertain-aware optimization of the system. Findings show that the polyhedral method provides more robust and confident results than the box method.

Distributed management of energy communities using stochastic profile steering

This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer’s profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer’s local energy management system. The outcome of the proposed method optimizes the average value of the community’s objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability.

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.

Flexible electricity consumption policies in Norway and Sweden: Implications for energy justice

Policies for shifting household electricity load are in focus in several countries, aimed at optimizing grid operation and tackling current and expected challenges from consumption patterns, like increased peak loads on the grid. Central here are recent developments in capacity charges, where the use of significant amounts of grid capacity is priced high, compared to the traditional pricing of electricity consumption. These capacity charges tend to be developed at the interface between system needs and incumbent actor influence from the sector on the one side, and arguments for a ‘just’ and consumer-oriented design, like simplicity and actual effect on behaviour, on the other. Comparing the consultation processes concerning policy design in Norway and Sweden, this study investigates if and how these capacity charge policies consider consumer perspectives and needs, and explores the implications for energy justice. The two countries have similar tariff models, but differing consumer considerations have become apparent. In both countries, the policy process has been dominated by sector incumbent actors; the consumer considerations that were included were weak, general, and not grounded in knowledge. Consumer representation has been low. In this study, we note the implications for recognition, procedural, and distributional justice. Our findings indicate the need for further development of balanced policy processes within the energy sector.

Synergetic urbanism: a theoretical exploration of a vertical farm as local heat source and flexible electricity user

The urban energy transition requires innovative heating and cooling systems, as well as enhanced flexibility in electricity usage. This paper explores the theoretical potential for vertical farms to contribute to the energy transition by supplying residual heat to local district heat networks and flexible electricity usage. A stepped approach was used to design energy systems that achieve thermal energy balance through heat and cold exchange between a vertical farm and buildings within a specific Dutch neighbourhood. Furthermore, alternative lighting strategies for vertical farms were explored to reduce grid congestion and to respond to electricity price fluctuations, limiting the mismatch between electricity generation and demand. Compared to the baseline scenario, the energy system with an integrated vertical farm reduces overall energy use by 15 %, even when accounting for the farm's electricity use. By adopting intermittent lighting that is better aligned with electricity price fluctuations, the vertical farm obtained annual cost savings of 14 %. The integration of vertical farms into energy systems can, therefore, contribute to the urban energy transition by producing residual heat to balance thermal energy system and save money for growers by optimising LED operations to align with electricity price fluctuations, whilst producing fresh vegetables for the city.

Different energy storage techniques: recent advancements, applications, limitations, and efficient utilization of sustainable energy

In order to fulfill consumer demand, energy storage may provide flexible electricity generation and delivery. By 2030, the amount of energy storage needed will quadruple what it is today, necessitating the use of very specialized equipment and systems. Energy storage is a technology that stores energy for use in power generation, heating, and cooling applications at a later time using various methods and storage mediums. Through the storage of excess energy and subsequent usage when needed, energy storage technologies can assist in maintaining a balance between generation and demand. Energy storage technologies are anticipated to play a significant role in electricity generation in future grids, working in conjunction with distributed generation resources. The use of renewable energy sources, including solar, wind, marine, geothermal, and biomass, is expanding quickly across the globe. The primary methods of storing energy include hydro, mechanical, electrochemical, and magnetic systems. Thermal energy storage, electric energy storage, pumped hydroelectric storage, biological energy storage, compressed air system, super electrical magnetic energy storage, and photonic energy conversion systems are the main topics of this study, which also examines various energy storage materials and their methodologies. In the present work, the concepts of various energy storage techniques and the computation of storage capacities are discussed. Energy storage materials are essential for the utilization of renewable energy sources and play a major part in the economical, clean, and adaptable usage of energy. As a result, a broad variety of materials are used in energy storage, and they have been the focus of intense research and development as well as industrialization. This review article discusses the recent developments in energy storage techniques such as thermal, mechanical, electrical, biological, and chemical energy storage in terms of their utilization. The focus of the study has an emphasis on the solar-energy storage system, which is future of the energy technology. It has been found that with the current storage technology, the efficiency of the various solar collectors was found to be increased by 37% compared with conventional solar thermal collectors. This work will guide the researchers in making their decisions while considering the qualities, benefits, restrictions, costs, and environmental factors. As a result, the findings of this review study may be very beneficial to many different energy sector stakeholders.

Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price

Accurate load control is the basis of safe operation and optimal dispatching in active distribution network, and the demand response system of flexible electricity price mechanism and incentive mechanism is the premise of load control. Aiming at the complexity increase of load control caused by the integration of conventional loads, electric vehicles, and micro-grid multi-type user loads in the existing active distribution network, this study comprehensively takes the power consumption willingness of multiple typesof users in active distribution network into account for the first time. This study dominated by the user willingness selects a typical active distribution network load-related data onto a certain area to conduct a calculation example analysis. Firstly, a multi-type user load demand response model of active distribution network is constructed. Then, in order to achieve flexible control of active distribution network loads, a dynamic time-of-use electricity price game model considering the target function and the willingness of multiple usersis proposed, in order to achieve the maximum of user satisfaction and interests of power supply enterprises. Furthermore, the example results are compared with the intrinsic electricity price.Finally, the simulation results show that the time-of-use price established by the multi-type user load game is effective inguiding users to adjust their power consumption habits, making the load curve shift, controlling the electricity load within the maximum power supply limit, and thus alleviating the pressure of power grid expansion.

Decoding design characteristics of local flexibility markets for congestion management with a multi-layered taxonomy

Local flexibility markets are becoming increasingly popular smart grid solutions. They connect customers who require flexible electricity supply and demand with local flexibility providers. However, the growing number of diverse solutions has led to a proliferation of concepts, projects, and companies in this market, with this diversity making understanding and comparison difficult. To tackle this challenge, we propose a multi-layered taxonomy of local flexibility market solutions. This focuses on congestion management on the distribution side of this activity; a crucial service for distribution system operators. Our taxonomy utilizes the Smart Grid Architecture Model to describe these markets comprehensively. We employ an iterative taxonomy-building method, refining and evaluating it through insights from ongoing implementations and twenty-eight expert interviews. Moreover, we present a complete instantiation of our taxonomy and offer a discussion with practical recommendations for practitioners in the local flexibility market landscape.

Explainable Approaches for Forecasting Building Electricity Consumption

Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes.

Semisupervised Learning-Based Occupancy Estimation for Real-Time Energy Management Using Ambient Data

Occupancy status information is essential for efficient energy management of deferrable and flexible electricity loads in both residential and commercial sectors. Motivated by the hardness to collect ground-truth occupancy data, we propose a new, semi-supervised learning based occupancy estimator, the Label-expanded Time-series Propensity Weighted Estimator (LTPWE), to improve the prediction accuracy using privacy-preserving ambient sensing data (in temperature, humidity, light, etc.), especially when the labelling frequency (the ratio of the labeled positive instances) is low. The proposed energy management scheme integrates the LTPWE occupancy estimator into a data-driven deep reinforcement learning algorithm, the Soft Actor Critic (SAC), to make real-time scheduling decisions without any prior knowledge on the dynamics of random renewable generation and electricity price. Simulation results on real-world datasets show that compared with multiple state-of-the-art baselines, the proposed energy management scheme can reduce the energy cost by 18.79-55.79% without sacrificing occupants’ thermal comfort.

Analysis of the impact of demand response on the Norwegian energy system

AbstractEuropean CO2 reduction goals have led to an increase in variable energy sources such as wind and solar, and consequently to an energy system that will need more flexibility in the future. In Norway, the hydropower reservoirs will enable the country to play a crucial role in European electrification by delivering flexibility to countries in Northern Europe. A further source of flexibility is demand response (DR) accumulated in residential, commercial, and industrial sectors. The paper discusses DR, load shifting, and load shedding based on the application of a stochastic TIMES model and it evaluates the role of DR in the Norwegian energy system towards 2050. The analysis shows that cost-efficient DR operation primarily comes from space heating in residential buildings. The use of DR, which is season-dependent, increases the volume of electricity trade, including electricity export and import to neighboring countries, and it smooths electricity prices. The implementation of DR in Norway leads to decreases in expected electricity price and total system cost by exporting flexible electricity and importing low price electricity. Additionally, it affects hydropower and reservoir management.

Analysis of critical peak electricity price optimization model considering coal consumption rate of power generation side.

As a flexible electricity pricing mechanism, critical peak pricing (CPP) is one of the important means of demand response under the electricity market. The existing CPP research does not take into account the carbon emission problem of units and weakens the difference between the use of terminal loads on critical peak days and non-critical peak days in the establishment of electricity price model, so this paper studies the feedback mechanism of CPP on coal consumption of power generation side units and proposes a dynamic CPP mechanism that takes into account terminal consumption satisfaction and coal consumption of power generation side units. Firstly, the influence mechanism of CPP on the power generation side is studied. Secondly, the consumer psychology theory is used to construct a user demand response model under critical peak days and non-critical peak days. Then, based on the difference in load usage of end users on critical peak days and non-critical peak days, a multi-objective CPP optimization model that considers the benefits of coal consumption and end user electricity expenditure on the power generation side is constructed. Finally, three scenarios were established to analyze the sensitivity of the user demand response model parameters, terminal satisfaction constraints, prices, and rate restrictions on CPP pricing, load improvement, and unit coal consumption reduction and verify the model's effectiveness. The results show that the proposed CPP optimization model has a significant effect on load improvement and carbon emission reduction; the user response gradient, terminal satisfaction, prices, and rate restrictions have a greater influence on the model optimization results, while the threshold and saturation values have little influence on the model optimization results.

Using flexible energy system interactions amongst industry, district heating, and the power sector to increase renewable energy penetration

Sweden’s goal of 100% renewable electricity generation by 2040 requires investments in intermittent electricity production (e.g. wind power). However, increasing the share of intermittent electricity production presents challenges, including reduced flexibility of electricity production. A strategy for overcoming this issue is developing flexibility in electricity consumption. This study analyses the potential for using flexible industrial processes, heat pumps (HP), and combined heat and power (CHP) plants in Swedish district heating systems to increase the share of wind power capacity without compromising grid stability. The simulation tool EnergyPLAN was used to assess the potential contribution of these strategies. The analysis includes a range of annual wind power production between 45 and 60 TWh. The required electricity imports and critical excess electricity (that can neither be used nor exported due to transmission line limitations) were used to evaluate the system’s stability. Managing the operation of CHP plants, HPs, and industrial processes in a flexible way is challenging, but these strategies may still play a decisive role in increasing the share of renewable electricity production and reducing demand peaks in cities. Whilst HP regulation is better at reducing excess electricity production at lower wind power capacities (from 32 to 15% for the analysed interval of wind power production), CHP regulation becomes more relevant when wind power capacity increases (from 14 to 39%). Like HP regulation, flexibility in electricity demand in industrial processes had a greater percentage contribution at lower wind power capacities. Combining HP, CHP regulation, and flexible electricity demands in industry can reduce excess electricity production by 68–80% and electricity imports by 14–26%. Wind power contributing to grid stabilisation reduces excess electricity production but does not reduce electricity imports.