This study examines how fiscal governance structures influence corporate environmental performance by exploiting Chinaâs 2003 tax delegation reform as a quasi-natural experiment. The reform transferred corporate income tax collection authority from locally-governed Local Tax Bureaus (LTBs) to centrally-managed State Tax Bureaus (STBs) based on a firm registration date cutoff of January 1, 2002. Using a Regression Discontinuity Design (RDD) with micro-level panel data from Chinese manufacturing firms (2004-2008), we identify the causal impact of tax administration assignment on firm-level energy efficiency, measured as output per unit of energy consumed. Our findings reveal that firms under LTB administration exhibit 8-12% higher energy efficiency compared to comparable firms under STB administration. This effect persists across multiple robustness checks, including alternative bandwidth specifications, placebo tests using unaffected firms, and alternative energy efficiency measures. Mechanism analysis demonstrates that the energy efficiency gains stem from three primary channels: (1) relaxed financial constraints enabling greater investment capacity, (2) transition toward cleaner energy sources with reduced coal dependency, and (3) increased adoption of energy-saving technologies and green innovation. These effects are particularly pronounced among financially constrained firms, non-exporters, and firms in regions with higher fiscal capacity or stronger environmental pressure. These results contribute to three strands of literature. First, they provide novel evidence that fiscal administrative structuresâtraditionally viewed as purely revenue instrumentsâcan have substantial unintended environmental consequences. Second, they demonstrate how local fiscal flexibility may create conditions conducive to green technological upgrading by alleviating financial frictions. Third, they extend the Porter Hypothesis to the institutional level, showing that supportive governance arrangements can simultaneously enhance economic efficiency and environmental sustainability. The findings suggest that integrating environmental performance metrics into local tax administration evaluation frameworks could align fiscal incentives with sustainability objectives, offering a promising pathway for emerging economies to achieve coordinated economic and environmental goals. âą LTB oversight improves firm energy efficiency by 8â12% over STB control. âą Lenient tax enforcement eases financing constraints for cleaner energy adoption. âą Environmental benefits are stronger in fiscally surplus or high-pressure cities. âą Financing-constrained and non-exporting firms benefit most from LTB regulation. âą Study links decentralized tax control to unexpected environmental improvements.
Clean energy transitions increasingly depend on the ability of small and medium-sized enterprises (SMEs) to access capital on terms that allow them to compete with large, vertically integrated incumbents. At a macro level, clean energy finance has evolved from subsidy-heavy public funding toward blended models combining private capital, risk-sharing instruments, and performance-based incentives. These structures aim to lower the cost of capital, correct market failures, and accelerate diffusion of renewable technologies across national energy systems. However, capital markets continue to privilege scale, balance-sheet strength, and long operating histories, creating persistent financing asymmetries that disadvantage smaller firms. This study situates clean energy financing within broader frameworks of financial inclusion, industrial competitiveness, and energy market liberalization. It examines how innovative financing architectures such as blended finance vehicles, green credit guarantees, pay-as-you-save schemes, revenue-backed project finance, and aggregated procurement platforms reshape risk allocation and margin dynamics. By reducing upfront capital requirements, smoothing cash flows, and improving bankability, these models enable SMEs to price energy products and services competitively while maintaining sustainable margins. Narrowing to the national context, the analysis highlights how policy design, regulatory certainty, and domestic financial infrastructure determine whether financing innovations translate into real competitive parity. Case-informed synthesis shows that when concessional capital is strategically deployed to crowd in commercial lenders, small enterprises can achieve cost structures comparable to larger incumbents, expand market share, and drive decentralized energy adoption. The findings underscore that clean energy competition is not solely a technological challenge, but a financial architecture problem, where well-designed financing models are decisive in leveling margins and unlocking inclusive energy-led growth at national scale under diverse regulatory and macroeconomic conditions globally relevant insights.
The construction industry is a major global consumer of energy and a leading source of greenhouse gas emissions, underscoring the need for transparent, data-driven, and energy-efficient supply chain strategies. This study develops an integrated mixed-integer linear programming (MILP) model for a multi-echelon, multi-product construction supply chain that explicitly incorporates differentiated building energy efficiency levels ( A +, A ++, A +++) as exogenous determinants of material requirements, production processes, and logistics flows. By embedding blockchain-enabled smart contracts, the model automates supplier governance and ensures compliance with delivery reliability, quality standards, and CO 2 performance through predefined incentives and penalties, thereby enhancing transparency and accountability. The framework jointly optimizes facility location, material and product flows, supplier selection, and reverse logistics operations under a COâ emission cap, while simultaneously capturing the implications of greenfield and brownfield project conditions. A real-scale numerical case study demonstrates the modelâs ability to evaluate the economicâenvironmental trade-offs arising from increasingly stringent sustainability requirements. The results reveal that although higher energy efficiency levels incur greater initial supply chain costs due to advanced materials and more complex logistics, they lead to substantial reductions in long-term operational energy consumption, rendering the A +++ option the most economically favorable from a lifecycle perspective. Furthermore, the integration of blockchain-enabled smart contracts partially offsets cost escalations by penalizing non-compliant suppliers and rewarding high-performing ones. Overall, the proposed model provides a rigorous and transparent decision-support framework that enables contractors to align supply chain design with energy-efficiency targets, CO 2 -reduction policies, and circular-economy objectives while preserving operational feasibility and supply reliability.
A lack of data security and interoperability are significant challenges in low-carbon building design due to the involvement of numerous architects, engineers, and carbon auditors. This study proposes a novel framework that integrates blockchain, building information modelling (BIM), and life cycle assessment (LCA) to enhance data management and decision-making for low-carbon building design. Blockchain, as a distributed ledger, ensures data security by maintaining an immutable record of architectural design changes, energy system configurations, and carbon footprints. BIM visualises all the design information in a three-dimensional model to ensure real-time updates of energy and carbon performance. LCA assesses the trade-off between embodied carbon and operating carbon emissions across a buildingâs life span, optimising architectural and energy system design decisions. The proposed framework establishes a secure and transparent workflow by integrating blockchain, BIM, and LCA into an iterative design process. Smart contracts facilitate the automatic verification of design parameters, enabling architects and engineers to adjust their designs when carbon footprint targets and energy-saving requirements are unmet. Additionally, the framework incorporates a database of future weather profiles, material properties, and inventory information to support accurate and informed decision-making. This study enhances data security, improves interoperability and supports the iterative refinement of low-carbon building designs. The framework was validated on an industrial building project, demonstrating its effectiveness in designing low-carbon buildings and its potential to contribute to global net-zero ambitions. By offering a structured approach to integrating blockchain, BIM, and LCA, this study offers valuable insights for practitioners and researchers in the architecture, engineering, and construction industries.
Aiming at the critical challenges of fragmented environmental-economic value tracking and inefficient multi-stakeholder coordination in green electricity trading, this study proposes a blockchain-based collaborative management method integrating environmental attributes (e.g., carbon offsets) with economic transactions. Leveraging blockchainâs decentralized, tamper-proof distributed ledger, the method ensures transaction transparency, automates settlement via smart contracts, and establishes a verifiable audit trail for environmental benefits. Experimental comparisons demonstrate that the blockchain platform âreduces transaction costs by 30%, shortens settlement time by 75%, and significantly enhances market liquidity and transparency versus traditional modes. This approach optimizes resource allocation, minimizes intermediary dependencies, and provides a robust technical pathway for scaling green power adoption. Key implementation barriers include blockchainâs energy consumption, smart contract vulnerabilities, and regulatory fragmentation across jurisdictions. Future work will focus on enhancing blockchain energy efficiency and developing cross-regional regulatory frameworks for green power markets.
Hossein Naderi, Alireza Shojaei, Mohammad Hossein Heydari
Building energy efficiency programs face significant challenges in performance monitoring and occupant engagement, which hinder the achievement of sustainability goals in the built environment. Traditional systems often suffer from intermediary-dependent workflows, insufficient transparency, and reliability issues, leading to conflicts among stakeholders and reduced occupant participation. This study proposes a blockchain-enabled solution that leverages Non-Fungible Tokens (NFTs) to improve the transparency, reliability, and traceability of performance monitoring systems. By integrating Digital Twin (DT) technology, blockchain, and a token marketplace, the platform not only enhances monitoring capabilities but also incentivizes occupants to adopt energy-efficient behaviors through Fungible Token (FT) rewards. A proof-of-concept prototype was developed using a synthetic case study, demonstrating the feasibility, cost efficiency, and scalability of the framework. The findings emphasize the importance of network selection for wider blockchain adoption. This transparent and immutable framework addresses key challenges in energy performance monitoring, offering a foundation for advancing sustainability in the built environment.
The rapid adoption of blockchain technology and generative AI contributes significantly to global electricity consumption, raising concerns about environmental sustainability. The first step in saving energy is to identify current consumption. However, since blockchain and generative AI are cloud-based services, it is difficult to understand electricity consumption outside oneâs facilities. This creates a barrier for user companies and organizations seeking to increase the accuracy of calculating Scope 3 emissions. This study quantifies the electricity consumption of these technologies at a system-wide and per-use level. It compares them to traditional services such as payment networks and web search engines. Bitcoin, a Proof of Work (PoW) blockchain, consumes approximately 121 TWh, equivalent to 0.43% of global electricity consumption, and its energy demand per transaction is 720,000 times higher than that of the Visa payment system. Ethereumâs move to Proof of Stake (PoS) in 2022 reduces energy consumption by 99.988%, demonstrating the potential for efficiency gains. Generative AI models also have significant energy requirements, especially during the training and inference phases. For example, training GPT-4 required approximately 9450 MWh, and daily inference work exceeded 500 MWh. The results show that inference, driven by frequent user interaction, often exceeds the energy consumption of training. The study underscores the urgency of addressing these technologiesâ environmental impact through strategies such as adopting energy-efficient consensus mechanisms and optimizing AIâs lifecycle. These findings are intended to guide organizations in refining their Scope 3 emissions calculations and adopting sustainable technology practices.
Oghenerume Augoye, Adekunle Adewoyin, Olugbenga Adediwin, Audu Joseph Audu
Artificial Intelligence (AI) is transforming energy financing by enhancing decision-making, optimizing investment portfolios, and improving risk assessment in sustainable infrastructure projects. This review explores the role of AI in energy financing, focusing on its applications in risk evaluation, credit scoring, investment optimization, and the development of climate-aligned financial strategies. AI-driven predictive analytics enable investors to assess the financial viability of renewable energy projects, identify high-impact opportunities, and optimize asset allocation. Additionally, AI-powered models enhance credit scoring for energy developers, facilitating access to funding for clean energy initiatives. The integration of AI with blockchain and smart contracts is also revolutionizing energy financing by ensuring transparency, reducing fraud, and automating financial transactions in sustainable projects. Furthermore, AI plays a crucial role in the management and monitoring of green bonds, improving impact assessment and ensuring accountability in climate finance. However, several challenges hinder AI-driven energy financing, including data limitations, regulatory gaps, cybersecurity risks, and potential biases in AI decision-making models. Ensuring data quality, developing ethical AI frameworks, and addressing cybersecurity concerns are essential for AIâs successful adoption in energy investment strategies. Future opportunities lie in AI-driven predictive analytics for emerging markets, enabling better financing mechanisms for off-grid and decentralized energy solutions. AI can also enhance public-private partnerships by optimizing investment structures and improving government funding allocation for renewable energy projects. As AI continues to evolve, it holds the potential to reshape energy financing, drive sustainable investments, and accelerate the transition to a low-carbon economy. This review underscores the need for collaborative efforts among policymakers, financial institutions, and technology providers to maximize AIâs potential in sustainable energy infrastructure financing while addressing its inherent challenges.
Decarbonization involves a large-scale expansion of low-carbon generators such as wind and solar and the electrification of heating and transport. Both space heating and battery-electric cars have significant embedded flexibility potential. Granular price signals that convey abundance or scarcity of electricity are a precondition for customers or aggregators acting on their behalf to exploit this flexibility. However, unmitigated real-time prices expose customers to electricity price risks. To tackle the dual need of providing flexibility incentives while protecting customers from cost shocks, real-time tariffs with a hedging component can be a solution. In such contracts customers pre-agree an amount of energy and a consumption profile, while hourly deviations are charged at spot prices. In this paper we analyze design options by using a dataset of anonymized smart meter data and show that profile tariffs can bring electricity bill volatility to similarly low levels as fixed tariffs while providing full flexibility incentives from spot prices. âą Profile contracts reduce bill volatility to similar levels as fixed price contracts. âą Profile contracts restore flexibility incentives suppressed by fixed price contracts. âą Profile contracts may reduce bill of flexible customers compared to fixed prices. âą Demand for profile contracts expected to increase as load flexibility increases.
This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous dayâs bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the programâs effectiveness in reducing both net energy costs and peak load on the electricity grid.
Mohamed Nour El-Din, João Poças Martins, Nuno Ramos, Pedro F. Pereira
Energy performance-based contracts (EPCs) offer a promising solution for enhancing the energy performance of buildings, which is an overarching step towards achieving Net Zero Carbon Buildings, addressing climate change and improving occupantsâ comfort. Despite their potential, their execution is constrained by difficulties that hinder their diffusion in the architecture, engineering, construction, and operation industry. Notably, the Measurement and Verification process is considered a significant impediment due to data sharing, storage, and security challenges. Nevertheless, there have been minimal efforts to analyze research conducted in this field systematically. A systematic analysis of 113 identified journal articles was conducted to fill this gap. A paucity of research tackling the utilization of digital technologies to enhance the implementation of EPCs was found. Consequently, this article proposes a framework integrating Digital Twin and Blockchain technologies to provide an enhanced EPC execution environment. Digital Twin technology leverages the system by monitoring and evaluating energy performance in real-time, predicting future performance, and facilitating informed decisions. Blockchain technology ensures the integrity, transparency, and accountability of information. Moreover, a private Blockchain infrastructure was originally introduced in the framework to eliminate high transaction costs related to on-chain storage and potential concerns regarding the confidentiality of information in open distributed ledgers.
Ivars KudreĆickis, Raimonds ErnĆĄteins, LÄ«ga BieziĆa, Rasa Ikstena
The article analyses the results of the 2016-2023 national programme of multi-apartment building renovation in Latvia, being importantly co-financed by ERDF, and there was used the publicly available database of this programme implementation, as of 31st December 2023. This was complementary analysed during the six deep semi-structured interviews with main stakeholders and experts at the municipal level particularly. Valmiera city and county municipality was chosen for a case study as one of the most pro-energy active municipalities in the country, having developed and introduced a complementary set of energy governance instruments. The challenges are particularly related to the management and economic aspects and their interconnection with engineering ones as being identified. Within 2016-2023 in total around 22.8 thousand apartments (over 620 buildings) are renovated at national scale, however that is only around 4% of the total number of apartments. The planned thermal energy savings constitute around 0.9% of the total final energy consumption of the household sector in Latvia as being the high impact. High energy efficiency for heating is achieved in the renovated buildings (after renovation, the âBâ energy efficiency class is achieved on average), however, the low number of renovated buildings still limits the impact of the programme. The renovation projects have a long (around 30 years in average) payback period, if compared with the actual district heating tariffs, thus, such renovation is hardly possible without the public grant part. Particularly, for completed projects in 2023 the specific costs significantly increase. The renovation of apartment buildings is analysed in the context of the energy citizenship (ENCI) concept. About 60% of building renovation are carried out by the legal institutional forms established by apartment owners, particularly, housing associations registered as NGOs. During the renovation of buildings, zero-emission decentralized energy production technologies are not installed until now, only few examples can be noted. Although the requirements of this renovation programme allowed, it could be assumed that the overall management and economic conditions were not enough attractive for the promotion of pro-sumerism for households or organizations to practice both - produce and consume energy.
Jan W. Bleyl, Mark Robertson, Sarah Mitchell, Patrik Thollander
Abstract Energy efficiency (EE) is our âfirst fuelâ and an essential resource in reaching climate goals, reducing dependence on fossil fuels, increasing security of supply, and many other âMultiple Benefits.â However, by their nature, savings are intangible. Demand-side EE measures are typically decentralized, heterogeneous, and small-scale opportunities. The difficulties in measurement and verification (M&V) of âNegawattsâ are an important and often overlooked barrier to their greater application. M&V is a prerequisite to assess the performance of any energy, water, or CO 2 -saving measure, and to quantify the savings into physical and monetary units for reporting, re-financing, GHG accounting, or other purposes. However, in practice, M&V is often perceived (particularly by clients) as cumbersome, incomprehensible, and costly. In the broader context, energy cost savings alone are often not a sufficiently strong project driver because they lack strategic relevance for decision makers. As âMultiple Benefitsâ of EE become better understood, the value of quantifying savings to a high degree of accuracy may be declining, creating opportunities for more flexible M&V standards. As a new methodology, this conceptual paper proposes to combine simplified M&V (sM&V) for individual EE measures with quality assurance instruments (QAIs) to verify functionality. This âsM&V + QAIâ approach is less cumbersome, less costly, and easier to comprehend than standard M&V approaches, particularly by clients, financiers, and other non-M&V experts. It has been reviewed by international experts and successfully tested and evaluated in the field. Multiple case studies are reported to verify its practical feasibility.
In this research, we analyzed three different models for Ethereum price prediction: a custom Hidden Markov Models (HMM), GHMM Pretrained, Deep Reinforcement Learning and LSTM. Our results demonstrate the distinct strengths and weaknesses of every model. Although HMM and HMM Pretrained excel in capturing volatility and short-term price fluctuations, the custom model demonstrates remarkable predictive capabilities for long-term trends. The present study provides significant contributions to the field of cryptocurrency price prediction, hence assisting traders, investors, and scholars in maneuvering through the complex Ethereum market.
In most domestic buildings, gas and electricity are supplied by energy and utility companies through centralised energy systems. This often results in a high burden on central management systems and has adverse effects on energy prices. Blockchain-based peer-to-peer energy trading platforms can deliver strategic operation of decentralised multi-energy network among multiple domestic buildings to reduce global greenhouse gas emissions and address global climate change issues. However, prevailing blockchain-based energy trading platforms focused on system implementation for peer-to-peer electricity trading while lacking predictive control and energy scheduling optimisation. Therefore, this paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy (i.e., heat and electricity) allocation and transmission, among multiple domestic buildings. Machine learning is harnessed to predict day-ahead energy generation and consumption patterns of prosumers and consumers within the multi-energy network. The proposed blockchain and machine learning-based decentralised energy management framework will establish optimal and automated energy allocation among multiple energy users through peer-to-peer energy transactions. This approach focuses on energy-matching from both the supply and demand sides while encouraging direct energy trading between prosumers and consumers. The security and fairness of energy trading can also be enhanced by using smart contracts to strictly execute the energy trading and bill payment rules. A case study of 4 real-life domestic buildings is introduced to determine the economic and technical potential of the proposed framework. In comparison to prevailing approaches, a key benefit from the proposed approach is an improved computational load/failure of a single point, energy trading strategy, workload, and capital cost energy. Findings suggest that energy costs reduced between 7.60%-25.41% for prosumer buildings and a fall of 5.40%-17.63% for consumer buildings. In practical applications, the proposed approach can involve a larger number of prosumer and consumer buildings within the community to decentralise multiple energy trading, thus significantly contributing to the reduction of greenhouse gas emissions and enhancing environmental sustainability.
Buildings can become a significant contributor to an energy systemâs resilience if they are operated in a coordinated manner to exploit their flexibility in multi-carrier energy networks. However, research and innovation activities are focused on single-carrier optimization (i.e., electricity), aiming to achieve Zero Energy Buildings, and miss the significant flexibility that buildings may offer through multi-energy coupling. In this paper, we propose to use blockchain technology and ERC-1155 tokens to digitize the heat and electrical energy flexibility of buildings, transforming them into active flexibility assets within integrated multi-energy grids, allowing them to trade both heat and electricity within community-level marketplaces. The solution increases the level of interoperability and integration of the buildings with community multi-energy grids and brings advantages from a transactive perspective. It permits digitizing multi-carrier energy using the same token and a single transaction to transfer both types of energy, processing transaction batches between the sender and receiver addresses, and holding both fungible and non-fungible tokens in smart contracts to support energy marketsâ financial payments and energy transactionsâ settlement. The results show the potential of our solution to support buildings in trading heat and electricity flexibility in the same market session, increasing their interoperability with energy markets while decreasing the transactional overhead and gas consumption.
Abstract Combined heat and power (CHP) systems, an effective way of meeting high energy demand with high efficiency, were adapted to existing large service buildings in most studies. Because of the large daily and seasonal fluctuations in energy and electricity demands of the buildings, optimization problems have come into view and have been studied. This study propounds a holistic design solution for the CHP systems' inadequacy to meet varying consumer energy demands in residential and the excessive electricity demand created by cryptocurrency mining. In addition, the study defines the possible high efficiency and sustainability for residentials producing and consuming heat and electricity by themselves. An energyâconservative threeâblock residential and a CHP system were projected together by a holistic view balancing the residencies' peak thermal demand to the thermal output capacity of the CHP system. The investment return rate of the CHP system was maximized by optimizing the thermodynamic efficiencies and maximizing the electric generation for cryptocurrency mining. The energetic efficiency increased from 40% to an energy utilization factor of 83%, and exergetic efficiency increased from 39% to 42%. The wasteâexergy ratio decreased from 61% to 58%. The engine's environmental effect factor was 1.56 and reduced to 1.38 with the CHP system. The exergetic sustainability index was improved from 0.64 to 0.72. The benefitâcost ratio was estimated as 1.6, with an internal return rate of 79%. Holistic designs considering common values should be considered to improve sustainability.
Price-responsive demand and dynamic electricity price contracts can play a vital role in balancing renewable energy production and alleviating energy shortages such as those experienced in the European energy crisis. This study focuses on the implicit demand flexibility of residential consumers during extraordinarily high electricity prices in winter 2021/22 in Norway where most households have electric heating and spot price contracts. An econometric model is developed that compares the demand with pre-crisis levels, adjusts for factors influencing electricity consumption, such as outdoor temperature, and utilises a comprehensive dataset including hourly electricity demand data. The results reveal a quick response since the price signal was passed immediately to the customers and substantial energy savings of 11.4 % during winter. While the average household showed no significant short-term price response to daily or hourly price variations, several subgroups did. Particularly, households actively monitoring hourly prices via real-time information channels and those with automatic smart charging of electric cars showed higher load reductions in peak price hours and load shifting to low-price hours. Thus, the study concludes that households are able to respond to variable hourly electricity prices and suggests the promotion of spot price contracts to incentivise residential demand response.
Joanna Gusc, Peter Bosma, SĆawomir Jarka, Agnieszka Biernat-Jarka
The current energy prices do not include the environmental, social, and economic short and long-term external effects. There is a gap in the literature on the decision-making model for the energy transition. True Cost Accounting (TCA) is an accounting management model supporting the decision-making process. This study investigates the challenges and explores how big data, AI, or blockchain could ease the TCA calculation and indirectly contribute to the transition towards more sustainable energy production. The research question addressed is: How can IT help TCA applications in the energy sector in Europe? The study uses qualitative interpretive methodology and is performed in the Netherlands, Germany, and Poland. The findings indicate the technical feasibilities of a big data infrastructure to cope with TCA challenges. The study contributes to the literature by identifying the challenges in TCA application for energy production, showing the readiness potential for big data, AI, and blockchain to tackle them, revealing the need for cooperation between accounting and technical disciplines to enable the energy transition.
Typically, residential buildings neither allow flexibility in the individual contract power capacity nor considers buildings as unique electricity consumers. In this work, a smart building is designed that each electricity customer has flexible contract power and the whole collective residential building has a single contract power. A management entity is considered to manage all energy resources of the building such as the photovoltaic generation, electric vehicles, and battery energy storage system, taking into consideration the consumption from apartments and common services, to minimize the electricity bill. Hence, the best/optimal contract power capacity will contribute to minimizing electricity costs. Therefore, finding the optimal decision of the contract power value has received a significant role from the energy management in smart buildings. In this paper, a mixed binary optimization problem is formulated in which not only the optimal value of contract power is yield but the optimal schedule of the electric vehicle/battery storage charge and discharge are found, taking into consideration the photovoltaic generation and load consumption profiles. The proposed model is implemented for three scenarios, and the obtained results show that the model efficiency has a high performance with a significant electricity cost reduction, around 47%. The results pointed that using an optimal value of single contract power and intelligent management system, the building electricity costs decrease remarkably.
Smart homes, connected through a network, can optimize the energy consumption and general load shape of their area. In this work, a blockchain-based smart solution is presented for demand-side management of residential buildings in a neighborhood to improve Peaks to Average Ratios (PAR) of power load, reduce energy consumption, and increase the thermal comfort of occupants by modeling heating, illumination, and appliance systems. For real-time power and temperature monitoring of the neighborhood, a transient numerical physical model has been developed. The simulator has been validated with data measured from a building in Northern Italy. Then, a neighborhood with 2,000 households has been modeled for different occupancy patterns, initial values, and boundary conditions. Two different control scenarios, namely basic and smart, have been considered. In the basic scenario, everything is managed by occupants except the boiler, which is controlled by the indoor temperature of the home. Instead, in the smart scenario, a blockchain-based network has been introduced for buildings to exchange a parameter called the Probability of the Next Hour (PNH). Ethereum Solidity has been deployed for smart contract development in the blockchain. The results show that using blockchain-connected smart controllers aimed at demand-side management can improve PAR, comfort level, and energy efficiency of buildings, which can bring about CO2 reduction on an urban and even global scale.
Yuta Susowake, Hasan Masrur, Tetsuya Yabiku, Tomonobu Senjyu · 7 authors
In Japan, residents of apartments are generally contracted to receive low voltage electricity from electric utilities. In recent years, there has been an increasing number of high voltage batch power receiving contracts for condominiums. In this research, a high voltage batch receiving contractor introduces a demandâresponse in a low voltage power receiving contract, which maximizes the profit of a high voltage batch receiving contractor and minimizes the electricity charge of residents by utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimization algorithm calculates a Pareto solution for the relationship between two objective trade-offs in the MATLAB Âź environment.