Smart Energy最新文献

In the context of the digital transformation of the electricity sector, the most recent technologies in power substations has promised benefits related to improved engineering, operations, and maintenance, transforming them into “smart” substations. However, methods and tools to analyse the impacts and estimate the benefits of electrical substation digitalisation to support investment decision-making have rarely been reported. We propose a comprehensive framework for assessing the impacts of the digitalisation of electrical power substations. Literature review, interviews with stakeholders and an expert survey were carried out to better understand those impacts and support the definition of the conceptual model. The framework identifies six key technological drivers and a total of 12 impacts, establishes the relationships between them, and defines key performance indicators for quantifying the impacts. The proposed framework was applied in a case study of the digital retrofit of a distribution substation on the scope of a research and development project on intelligent substations. The application demonstrates the framework's usefulness as a tool to systematically assess digital substations and highlights how digitalisation can benefit engineering and construction, and operations and maintenance activities. The proposed framework can also be applied to other contexts, thus contributing to an increasingly digital and connected electricity sector.

Sector integration is one of the major components considered when designing future smart energy systems. Due to the lack of data, current partitioning of consumers into residential, commercial, industrial, and transportation sectors is based on assumptions on their respective energy demand. In reality though, there is a diversity in preferences and behaviors so that consumers from the same sector may have different demand and consumers from different sectors may have similar demand. With the increasing amount of individual data, it is becoming possible to study this diversity and more accurately partition consumers using advanced analysis techniques such as clustering. However, while this approach does allow for an accurate grouping, the complex mechanisms at the roots of identified clusters are still unclear. Indeed, energy demand depends on multiple factors such as economic, political, cultural, social, or historical besides environmental conditions. The present study uses households' data provided by the Japanese Ministry of the Environment with the objective of finding patterns associated with residential energy consumption profiles. It is found that the probability distribution of households' energy consumption seems to be log-normal so that clusters are revealed by first applying a logarithmic nonlinear transformation. Furthermore, k-means clustering, which is commonly used in energy systems study, fails here to correctly identify the clusters when compared with density-based clustering. After identifying clusters, we look for statistically significant specificities in the corresponding households' data such as their geographical location, number of residents, income, buildings' construction year, equipment and vehicles and suggest interpretations for each.

This study investigates the cost balance between heat energy savings through building envelope retrofits and supply from low-carbon decentralised and centralised technologies in a generic urban district, composed of residential and non-residential buildings from the 1970–1989 construction period. For generalisability, the district is analysed in three European countries (Bulgaria, Germany, Finland), each with distinct weather conditions and price levels. Using bottom-up energy modelling and adopting a societal perspective that includes external costs, the study finds the cost-effectiveness of retrofits to be context-specific. In Bulgaria, retrofits prove largely cost-effective, whereas in Germany and Finland, high labour and material costs pose challenges. Heat pumps, whether decentralised in buildings or centralised in district heating systems, emerge as key options for heat supply, even in cold climates. The study underscores the importance of integrated energy planning in line with the ‘energy efficiency first’ principle and corresponding incentive structures to promote sustainable urban energy systems.

Most district heating (DH) networks are largely based on fossil or biogenic fuels. As these fuels are phased out or their use will be prioritized for other sectors respectively, significant amounts of alternative heat sources (heat pumps, waste heat, solar and geothermal energy) will be required. However, there are various uncertainties regarding the development of key factors such as energy prices and the availability of alternative heat sources. In addition, individual heat supply systems are competing with DH networks. This paper quantifies the economic risks of DH networks with respect to uncertainties in energy prices (electricity and biomass) and waste heat availability and compares them with individual heating systems. Therefore, a hypothetical inter-regional heat transfer network (“HTN”) in Austria is investigated as a case study and a Monte Carlo approach based on seasonal energy balances is used. The results show that in individual heating systems, uncertainties in energy prices have a strong influence on the economic risks. In contrast, HTNs can optimize the use of industrial waste heat at stable prices and integrate large scale heat pumps operating at low electricity prices as well as combined heat and power plants operating at high electricity prices, leading to a reduced dependency on the uncertainties of energy prices and thus a lower economic risk.

Integrated energy systems have recently gained primary importance in clean energy transition. The combination of the electricity, heating and gas sectors can improve the overall system efficiency and integration of renewables by exploiting the synergies among the energy vectors. In particular, real-time optimization tools based on Model Predictive Control (MPC) can considerably improve the performance of systems with several conversion units and distribution networks by automatically coordinating all interacting technologies. Despite the relevance of several simulation studies on the topic, however, it is significantly harder to have an experimental demonstration of this improvement. This work presents a methodology for the real-world implementation of a novel smart control strategy for integrated energy systems, based on two coordinated MPC levels, which optimize the operation of all conversion units and all energy vectors in the short- and long-term, respectively, to account also for economic incentives on critical units. The strategy that was previously developed and evaluated in a simulation environment has now been implemented, as a supervisory controller, in the integrated energy system of a hospital in Italy. The optimal control logic is easily actuated by dynamically communicating the optimal set-points to the existing Building Management System, without having to alter the system configuration. Field data collected over a two-year period, firstly when it was business as usual and when the new operation was introduced, show that the MPC increased the economic margin and revenues from yearly incentives and lowered the amount of electricity purchased, reducing dependency on the power grid.

As the penetration of electric vehicles (EVs) grows, it is important to understand the implications of home charging technologies for grid operations and for the budget of users. We conduct an empirical study analyzing data on 438 EVs over a period of 3,687 consecutive hours, collected by an energy aggregator which operates a digital platform. We first develop an optimization model to compute an optimal schedule of charging for all EVs in the dataset at minimum cost. Then, we compare the realizations against this optimal solution, distinguishing householders who use a smart charging functionality of the digital platform from those who do not use it. Our findings indicate that the smart charging behaviour conduces to better results, and close to the optimal solution. The non-users tend to start charging as soon as they plug-in their EVs, often at peak consumption times. In contrast, the smart charging strategy usually shifts the charging schedules towards times where the consumption is cheaper and the grid is less congested, facilitating a higher load factor and lower power losses. These results highlight the positive role of energy aggregators and digital platforms in coordinating users to lower the cost and enhance efficiency of energy consumption.

Sector coupling and system integration are key concepts in the energy transition from fossil fuels to fully decarbonized energy systems based on renewable energy. An intelligent use of sector coupling – such as that expressed in the concept of a smart energy system –accommodates for the identification of a more energy-efficient and affordable green transition. However, these benefits are often not fully identified in scenario modelling for the simple reason that not all energy systems analysis tools are equipped to do so. Here, we use the EnergyPLAN tool to replicate the EU Baseline and 1.5 TECH scenarios of the report “A Clean Planet for All”, which we then compare to a smart energy systems scenario for Europe. Due to its focus on sector coupling, we show how such a smart energy Europe scenario can be more energy efficient and affordable than the other scenarios.

Greenfield precincts offer an opportunity to develop energy hubs, which can help the transition to carbon neutrality. However, this requires a detailed demand model, which is often not available in the early planning stages. To address this need, this paper proposes a novel methodology for building a demand model using available information, including types of zones/sectors present on-site, historical energy consumption of those sectors at a national level, and energy consumption studies on floor-stock basis (i.e., floor area). We apply this approach in a case study energizing the proposed Aerotropolis Core Precinct (ACP) within the wider Aerotropolis site to be constructed in Western Sydney, New South Wales, Australia. The model also looks at supplying this demand, and the corresponding associated emissions, for the years 2025, 2035 and 2050 – in line with Australia’s Net Zero-time horizon. Results show that ACP demand increases from 368 GWh in 2025 to 1,233 GWh in 2035 then 1,444 GWh in 2050, as is expected moving from partial to full operation and accounting for temperature-dependent demand fluctuations. Electrical demand is 62% of total demand, while thermal is 38%. Generated supply is estimated to increase from 221 MW (2025) to 866 MW (2035) and 1,077 MW (2050), accounting for capacity factors. Emissions associated with that demand will change from 185 ktCO₂eq to 229 ktCO₂eq then decrease to 201 ktCO₂eq due to an increasingly “greener” technology mix predicted (i.e., no use of coal due to shutdowns of coal-fired power plants, and very little gas use by 2050). These methods and estimates provide a decision-making basis for government policy-making, energy planning, and technology supply for greenfield sites, as well as provide a platform to invite stakeholder engagement.

The representativeness of several important parameters, such as energy return on investment (EROI) and those requiring summing primary energy (PE), are often questioned due to the gap in PE quantification technique. This fundamental gap is systematically investigated in order to clarify the PE quantification problem, its impact, and propose a justifiable solution using a widely used tool, such as EROI, together with appropriate data and scenarios. The analysis shows that present PE estimation lacks scientifically justifiable grounds to compare any parameters that depend on it. For example, present EROI calculation is unsuitable for technology-to-technology or system-to-system or system-to-technology comparisons because of the variation of primary energy quality (PEQ) with a resource-technology combination. The main cause of PEQ discrepancy is the absence of reference energy quality that facilitates proper comparison and interconversion. This study shows that standardising PEQ enables a scientifically meaningful quantification of PE and a justifiable comparison of EROI as well as other relevant indicators depending on it. Electricity emerges as the best option for solving the differences in PEQ in the short-term. However, the logical long-term solution is to standardise the energy unit “joule” to attain a definite value, similar to kilogram, across the various sub-areas of energy.

Smart local energy systems (SLES) have been promoted in policy as a solution to decarbonisation challenges which also bring wider benefits, such as community prosperity and energy affordability. But the combination of conditions required to enable their successful emergence and operation are still to be elaborated. This paper reports on the development of a Theory of Change (ToC) for the “societal project” of emergence of SLES with benefits. ToC is a process of making explicit the causal links by which activities lead to outcomes, surfacing assumptions, and recognising possible unintended consequences. We describe the ToC development process, involving consultation and collaboration across a research consortium. It consists of layers (e.g. users, skills, data and digital), and shows conditions considered necessary to deliver SLES, and for these to deliver wider benefits. It also provides interactive links to evidence emerging from the consortium, as well as policy/governance conditions and metrics. We reflect on potential uses of the ToC – internally to the consortium and externally – along with challenges we encountered in pursuing this approach. Policy implications relate to the importance of enabling conditions across multiple sectors, the absence of any of which could inhibit delivery of either SLES or their ensuing benefits.