Virtual Energy Research Articles

Cryptocurrency mining as a novel virtual energy storage system in islanded and grid-connected microgrids

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Two-stage decision-dependent demand response driven by TCLs for distribution system resilience enhancement

Service restoration is a critical function of the self-healing distribution system to cope with extreme events. Cold load pickup (CLPU) caused by thermostatically controlled loads (TCLs) requires extra generation power to retore loads, slowing down the distribution system restoration process. Meanwhile, increasing renewable energy brings the supply-demand imbalance because of the power variability and may cause voltage or current violations. Accordingly, a two-stage decision-dependent demand response (DR) methodology is proposed to address the two barriers to distribution system resilience enhancement. Specifically, two load control strategies are developed in the first-stage DR to regulate TCLs for CLPU mitigation, which reduces the additional power requirement when re-energized. In the second-stage DR, the restored TCLs that can be controlled to provide flexibility are characterized as virtual energy storage (VES) for managing renewable energy variability. In this way, the required power generation is reduced, and the power fluctuation problem caused by renewable energy can be well addressed. It helps to speed up the load restoration and keep the distribution system operating securely. Moreover, the load profiles with CLPU mitigation control, VES parameters and VES availability time, depend on the re-energization time. In other words, load restoration decisions determine the two-stage DR. This is the most important finding of this paper and explicitly formulated in the load restoration model for accurate load characterization. The load restoration model is described by a mixed integer second-order cone programming (MISOCP) problem and solved using MATLAB 2021 with Gurobi solver. Case studies show that the proposed methodology can significantly mitigate CLPU and restore more loads. The decision-dependent behaviors of the two-stage DR are also validated.

Data-driven-based distributionally robust optimization approach for a virtual power plant considering the responsiveness of electric vehicles and Ladder-type carbon trading

To encourage the utilization of decentralized renewable energy systems, a data-driven-based distributionally robust optimization (DRO) model is proposed for a virtual power plant (VPP) considering the responsiveness of electric vehicles (EVs) and a ladder-type carbon trading mechanism (LT-CTM). An EV virtual energy storage (EV-VES) calculation model for dispatchable power is formulated to facilitate the centralized scheduling of EVs, considering responsiveness and spatial–temporal distribution characteristics. Additionally, multiple kinds of energy storage systems and flexible loads are considered in the VPP. Furthermore, to handle multiple uncertainties for both the renewable energy and load, Latin hypercube sampling is applied to generate the renewable energy scenarios, and K-means clustering is used to obtain typical scenes. Then the DRO model is used to solve the dispatch problem. An LT-CTM is leveraged to reduce carbon emissions. The column and constraint generation algorithm is then employed to identify an optimal operational benefit solution. Finally, a multi-parameter coupling analysis of ladder-type carbon trading is used for the environmental protection and economy of the VPP. The results confirm the efficacy of the proposed day-ahead scheduling model, offering a theoretical reference for operators when making scheduling decisions.

Effect of silica fume on fracture analysis, durability performance and embodied carbon of fiber-reinforced self-healed concrete

Nowadays concrete industry is trying to develop sustainable and energy efficient materials. One of the techniques is to develop crack free concrete. Therefore, this study focusses on the use of different supplementary materials like silica fume as substitute of cement to characterize the self-healing capacity of fiber-reinforced concrete in order to produce crack free concrete. The employed concrete consists of cement Type-I, synthetic fibers of polyvinyl alcohol (PVA) type with 0.5 % by the total volume of the mix, and two different types of self-healing materials. Cement was replaced by silica fume (SF) up to 15 % with an increment of 2.5 %, sand passing from sieve # 16 and CA of size 12 mm was used to produce high-strength concrete. Three-point bending test and microscopic examination were used to investigate the role of fibers on the self-healing efficiency of fiber-reinforced concrete specimens. Specimens were pre-cracked in the range of 50–100 μm at 14 days of curing and then, placed in water for 14, 56-days water condition to assess the effect of self-healing. Digital high-range microscope was used to measure crack width after pre-cracking and at the end of every exposure duration. After the specified exposure duration, specimens were microscopically observed again to check the crack healing and re-loaded up to final failure. Outcomes were taken in terms of peak load increase value after healing stage (FCPL), controlling crack propagation due to healing effect termed as (IFTR), effect of cracking on fracture energy in terms of (IFER) and Embodied Carbon and Embodied Energy. It was found from test results that the healing efficiency in terms of FCPL, IFTR, and IFER was improved, due to the use of silica fume as it has lower surface area then OPC, which triggers healing rate faster by accumulation of healed produces near crack surface in early age. Furthermore, durability performance in terms of embodied carbon and embodied energy is also reduced because of full-crack healing, which shows profound healing effect.

Exergy-driven optimal operation of virtual energy station based on coordinated cooperative and Stackelberg games

Virtual Energy Stations (VESs) can aggregate customer-side Integrated Energy Systems (IESs) to participate in electricity and natural gas markets. It helps to promote the synergies among different energies, lower IESs' energy cost, and balance the supply and demand of power systems. This paper proposes an exergy-driven optimal scheduling method of VES based on coordinated cooperative and Stackelberg games to regulate IESs via demand response (DR) effectively and economically. Firstly, the VES operation framework with equal exergy replacement is developed to unify the energy adjustment and conversion of IESs for less energy cost and lower economic loss risk. Secondly, the scheduling models of VES and IESs based on Cooperative and Stackelberg Games are proposed. VES forms a cooperative alliance with IESs to respond to external energy markets' price fluctuations for overall benefit maximization. Then the Stackelberg game theory is employed to characterize the interest-conflict behaviors of VES and IESs. It can maximize the overall interests of VES and IES, achieve more flexibility from IESs, and improve IESs' enthusiasm in DR. Thirdly, the equilibrium solution existence of the VES optimal scheduling model based on game theories is proved. A distributed solution method based on the Alternating Direction Method of Multipliers (ADMM) algorithm is provided to avoid the privacy information leakage of VES and IESs. Simulation results show that VES takes the main risks caused by the energy market prices' fluctuations, which helps to increase the enthusiasm of IESs in DR. And the proposed incentive mechanism has superior properties compared to the sole cooperative game or Stackelberg game.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

Optimal scheduling of an electric–hydrogen-integrated energy system considering virtual energy storage

In this paper, a two-layer optimization approach is proposed to facilitate the multi-energy complementarity and coupling and optimize the system configuration in an electric-hydrogen-integrated energy system (EH-IES). Firstly, an EH-IES with virtual energy storage is proposed to reduce the cost of physical energy storage equipment. Secondly, a two-layer optimal allocation method is proposed under a multi-timescale strategy to examine the comprehensive evaluation index of environmental protection and economy. The upper layer utilizes the NSGA-II multi-objective optimization method for system capacity allocation, while the lower layer performs economic dispatch at the lowest cost. Ultimately, the output includes the results of the equipment capacity allocation of the EH-IES that satisfies the reliability constraint interval and the daily scheduling results of the equipment. The results demonstrate that the electric-hydrogen-integrated energy system with the coupling of multiple energy equipment not only enhances the utilization of renewable energy sources but also reduces the usage of fossil energy and improves the system’s reliability.

Influence of urban density on energy retrofit of building stock: case study of Spain

ABSTRACT Energy retrofitting of existing buildings plays a key role in the European targets of Green House Gas reduction. The addition or substitution of construction materials is needed to improve energy performance of buildings and its environmental impact must be considered. This paper proposes a model for the assessment of the environmental impact of neighborhood retrofit in relation with urban density. The model is based on the decomposition of buildings into functional elements and the quantification of construction materials used in building energy retrofitting. As a novelty, the need of road rehabilitation was also considered. The environmental impact was assessed in terms of Embodied Energy (EE) and Embodied Carbon (EC). The operational energy after retrofit was also calculated. The results bring the evidence of the relation between the urban density and the EE and EC derived from building retrofit, which can be up to 60% higher in low density areas. The sensitivity analysis carried out demonstrates the influence of the urban compactness on the environmental assessment results. The model proposed can assist in the design of retrofit plans at urban level by highlighting those retrofit measures with most significant environmental impact depending on the urban density.

Learning passive policies with virtual energy tanks in robotics

AbstractWithin a robotic context, the techniques of passivity‐based control and reinforcement learning are merged with the goal of eliminating some of their reciprocal weaknesses, as well as inducing novel promising features in the resulting framework. The contribution is framed in a scenario where passivity‐based control is implemented by means of virtual energy tanks, a control technique developed to achieve closed‐loop passivity for any arbitrary control input. Albeit the latter result is heavily used, it is discussed why its practical application at its current stage remains rather limited, which makes contact with the highly debated claim that passivity‐based techniques are associated with a loss of performance. The use of reinforcement learning allows to learn a control policy that can be passivized using the energy tank architecture, combining the versatility of learning approaches and the system theoretic properties which can be inferred due to the energy tanks. Simulations show the validity of the approach, as well as novel interesting research directions in energy‐aware robotics.

Embodied impacts of buildings from energy-carbon-water nexus perspective: A case study of university buildings

Despite extensive investment in energy efficiency efforts, the energy footprint of the building sector still contains over 40 % of the world's annual consumption of energy. Although most of a building's life cycle energy use comes from operational activities, a portion of it stems from embodied energy (EE), which is directly expended in a building's construction and indirectly consumed using materials. Because each material consumes not only different amounts but also different types of energy sources, studying embodied carbon (EC) is equally important. The building construction sector also consumes nearly 1/5th of global fresh water, which is becoming a grave concern, given the increasing frequency of droughts and wildfires. The fact that material manufacturing and construction processes also consume water makes it essential to not just assess energy and carbon but also embodied water (EW). The literature recommends evaluating total EW including direct and indirect EW as well as any EW associated with EE use. In this paper, macroeconomic models are utilized to compute and analyze the energy, carbon emission, and water embodied in four university buildings. The results show that the total EE, EC, and EW values for the four case study buildings vary from 13.1 to 51.1 GJ/m2, 1.4–10.0 kgCO2/m2, and 2,820–12,900 gal./m2, respectively. The EE values also show a near perfect positive correlation with EW values. However, the share of EREW in the total EW ranges from 13 % to 16 %, indicating that a decrease in EE use may not help decrease a majority of EW.

Dynamic pricing optimization for commercial subcontracting power suppliers engaging demand response considering building virtual energy storage

Commercial buildings have abundant flexible energy resources for demand response (DR). The electricity price for tenants in the commercial building is generally issued by a subcontracting power supplier (SPS), and the tenants cannot directly interact with the energy retailer. Therefore, the incentive for tenants to participate in DR is insufficient, and their potential is not fully explored. To address these issues, this paper proposes a dynamic pricing method based on the Stackelberg game, helping tenants actively participate in DR. Then, with the optimized energy consumption of the tenants, a virtual energy storage model of the commercial building is constructed by aggregating the adjustable capabilities of flexible energy resources such as air-conditioning (AC) and electric vehicles (EVs) in the public area. Finally, simulation tests are conducted based on a real commercial building to demonstrate the effectiveness of the game-theoretic pricing approach and validate the role of virtual energy storage of the building in DR.

Increase the integration of renewable energy using flexibility of source-network-load-storage in district cooling system

The flexibility of power systems is critical to cope with the variability and uncertainty caused by the integration of renewable energy resources. Incorporating district cooling systems (DCSs) into power systems is an emerging approach to provide flexibility for renewable energy utilization. However, the existing models of DCS components, such as ice-storage cooling sources, cooling pipelines, and cold warehouses, are simplified, failing to fully utilize that flexibility, which brings difficulties for renewable energy utilization. Therefore, this paper proposed a coordinated operational strategy for an integrated power and cooling system combining detailed operating characteristics of DCSs to exploit the potential flexibility of DCSs for better integration of renewable energy. The primary cooling source components, including the chiller, ice storage tank, pump, and cooling tower, are established to model a detailed process of cooling generation and storage. The thermal inertial of district cooling networks (DCNs) is formulated as a quasi-dynamic model by considering dynamical temperature characteristics and transmission delay to exploit the virtual energy storage capacity of the cooling water pipeline. Moreover, the thermal inertia of the demand side, such as buildings and cold warehouses, is considered to utilize thermal storage capacity. The objective function of the proposed optimization model is to minimize the overall operating cost of the integrated power and cooling system. Numerical simulations show that the proposed method could reduce the operating cost by 15.4% and the wind curtailment rate by 86.2%, indicating better performance of the proposed method in improving economic benefits and reducing renewable energy curtailment.

Optimal scheduling of electric-gas-heat system considering dual virtual energy storage of gas network management and heat network storage

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas-heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium’s 20-node natural gas network and 6-node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power.

Spatiotemporal management of solar, wind and hydropower across continental Europe

Weather climate fluctuations cause large variations in renewable electricity production, which requires substantial amounts of energy storage to overcome energy drought periods. Based on daily hydroclimatic data and information about renewable power systems covering Europe, here we quantify the complementarity in the solar-wind-hydro energy components of the continental climate system. We show that the spatiotemporal management of renewable electricity production over Europe can induce a virtual energy storage gain that is several times larger than the available energy storage capacity in hydropower reservoirs. The potential electricity production matches the consumption by spatiotemporal management of suitable shares of solar and wind power complemented with the present hydropower. While the mixed renewable energy potential varies less than anticipated at the continental scale, utilization of the complementarity requires new continental electrical transmission lines and stable international trade. We highlight that management models need to consider incentives beyond national boundaries to appropriately benefit from continental climate conditions.

Embodied water-energy nexus towards sustainable building construction: Case of conventional Indian houses

Amid global water concerns and understanding ferocious water consumption in building construction entails attending to the construction embodied water (EW)- an infant realised domain. Let alone the practised embodied energy (EE); the study attempts to unleash a pragmatic guide to building construction sustainability through the EW-EE nexus. The cradle-to-gate study covers three conventional Indian houses in Jammu. It complies with general ISO-14044 and 14046 frameworks while consulting both-Indian and global material-wise EE and EW consumption databases. The hybrid approach adopted involves quota sampling, field-based investigations, descriptive statistics, and regression plots. The comprehensive system loop analysis seeks disaggregated (house-wise) and aggregated results. The Indian constructions reflect improved 7158 MJ/m2 EE but severe 16.7 kL/m2 EW vis-à-vis global scientific studies. Steel is no-longer the topmost EW-impacting material for Indian conventional houses, while stone aggregates, finishing, and decoration materials stand apart in elevating EW. Unearthing the weak EW-EE relationship underlines the incapability of EE-driven research to address EW effectively in the houses. As it stands, Architectural plus design (A + D) decision-making aiming to reduce EW also checks EE. Thus, EW deserves contemplation rather than EE alone in realising a holistic, sustainable built environment (SBE). Alongside consolidating EE research, the scarce EW domain receives much-needed dissemination. Besides striving to stimulate the scientific community, outcomes like the identified materials template are valuable for devising policy and A + D advocacy vis-à-vis EW-EE conservation. Seeking conventional building construction and design solutions through the EW-EE nexus is novel and meets multiple 2030 sustainable development goals.

Building Upcycling vs. Building Reconstruction: a Life Cycle Valuation for investment decisions

Decision-making processes concerning investments in building upcycling vs. building reconstruction of the existing stock involve environmental aspects besides the economic-financial ones. For example, material and energy preservation, waste management, Embodied Energy (EE), and Embodied Carbon (EC) management in construction processes are crucial aspects that concern different scales: material/component/system, whole building, urban scale, and civil engineering works and infrastructures. In the perspective of more restrictive norms on the displacement of materials with residual energy potential, EE and EC should be considered as hidden components of building value and, thus, internalized into investment decision-making processes under a circular perspective. Therefore, this contribution aims to present the first simulation of a methodological proposal to evaluate two alternative investment projects (a residential building upcycling vs. a reconstruction scenario) by internalizing environmental components in the financial Discounted Cash-flow Analysis (DCFA). From a life cycle perspective, the global cost and the «global benefit» are modeled using the NPV indicator calculation. The results highlight the weight of the environmental cost items from a financial perspective and the capability of environmental input (EE and CO2 emissions) to influence the results, thus orienting investment decisions and public policy-making towards sustainable design.

Emergy Evaluation of Ecological Engineering for Riparian Restoration in the Upper Reaches of the Yangtze River, China

Complex hydrological regimes and rugged topography in mountainous cities have increasingly compromised urban riparian zones, presenting marked restoration challenges. This study conducted restoration trials along the Jiulongtan riparian zone, located in the upper reaches of the Yangtze River (URYR), China. It featured 2 major ecological engineering interventions: (a) establishing semi-natural meadows and applying braided branched meshes to the riparian slope revetment and (b) constructing littoral woods and managing microtopography and sediment in the floodplain area. Furthermore, emergy evaluation was utilized to gauge the effectiveness of these restoration strategies and to assess the sustainability of the reconstructed riparian ecosystems. The following results were obtained: (a) Both the restored slope revetment (RSR) and restored floodplain (RF) ecosystems achieved a state of performance equilibrium after their implementation, with the former benefiting from renewable energy investments and the latter effectively utilizing local resources; (b) service yield outcomes highlighted significant enhancements in species diversity within the reconstructed ecosystems; plant diversity emerged as the primary contributor to total service yield, recording 3.71 × 10 18 sej/a for the RSR and 5.50 × 10 18 sej/a for the RF; (c) the emergy sustainability index values of 1.98 for the RSR and 5.16 for the RF suggest robust sustainability potential, particularly for the RF, even amid intense environmental stresses. These results affirm the success of the innovative restoration strategies employed for riparian reconstruction in the URYR’s mountainous urban settings, offering valuable insights and reference for future restorative decisions in response to complex hydrological regimes and topography of urban riparian zones, both within the URYR and elsewhere.

Notice of Violation of IEEE Publication Principles: An Energy-Efficient Heterogeneous Memory Architecture for Future Dark Silicon Embedded Chip-Multiprocessors

Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era causes a drastic increase in leakage power consumption and temperature-related problems. Emerging non-volatile memory (NVM) technologies offer many desirable characteristics such as near-zero leakage power, high density and non-volatility. They can significantly mitigate the issue of memory leakage power in future embedded chip-multiprocessor (eCMP) systems. However, they suffer from challenges such as limited write endurance and high write energy consumption which restrict them for adoption in modern memory systems. In this article, we present a convex optimization model to design a 3D stacked hybrid memory architecture in order to minimize the future embedded systems energy consumption in the dark silicon era. This proposed approach satisfies endurance constraint in order to design a reliable memory system. Our convex model optimizes numbers and placement of eDRAM and STT-RAM memory banks on the memory layer to exploit the advantages of both technologies in future eCMPs. Energy consumption, the main challenge in the dark silicon era, is represented as a major target in this work and it is minimized by the detailed optimization model in order to design a dark silicon aware 3D Chip-Multiprocessor. Experimental results show that in comparison with the Baseline memory design, the proposed architecture improves the energy consumption and performance of the 3D CMP on average about 61.33 and 9 percent respectively.

Integrating Virtual Reality and Energy Analysis with BIM to Optimize Window-to-Wall Ratio and Building’s Orientation for Age-in-Place Design at the Conceptual Stage

Integrating Virtual Reality and Energy Analysis with BIM to Optimize Window-to-Wall Ratio and Building’s Orientation for Age-in-Place Design at the Conceptual Stage

Developing A Universal Framework for Modeling DC-Connected Hybrid Real and Virtual Energy Storage Systems

Developing A Universal Framework for Modeling DC-Connected Hybrid Real and Virtual Energy Storage Systems