Smart Energy最新文献

Seasonal storage is a key feature of future decarbonized energy systems with a high share of renewable energy integration. Power-to-Gas technologies represent a promising solution to enable such storage. They allow the conversion of surplus renewable electricity into e-fuels and their storage in the long-term. Their utilization enables the integration of the electrical, fuel and heating sectors, by converting electricity into fuels and recovering the waste heat from the process. Nevertheless, to design the most profitable management strategy for such systems, advanced control tools are required. This study introduces a novel control architecture for multiple multi-energy systems that share an e-fuel seasonal storage. Each energy system has its own short-term control logic, based on Model-Predictive Control (MPC), which manages day-ahead energy exchanges, while a long-term MPC controller considers yearly dynamics and the system as a whole. This gives additional constraints to the short-term controllers, which ensure the fulfillment of yearly goals. A multi-temporal and multi-spatial hierarchical control architecture is proposed, which enables optimal seasonal storage management, and its operation is verified in a Model-in-the-Loop configuration. The controller efficiently uses seasonal storage to balance seasonal mismatch between production and demand, resulting in higher utilization of renewable energy, lower emissions and costs.

The value of energy system scenarios is increasingly asserted in a decentralised and municipal context. There is, however, a lack of suitable tools for designing such scenarios, particularly tools that empower local planning practitioners in active participation. With this study, we introduce a novel tool designed specifically for municipal energy system modelling, thus bridging the gap between model developers and planning practitioners. The applicability and suitability of the new MUSEPLAN tool is investigated through its application in a case municipality, revolving around the needs of planning practitioners, supporting the build-up of modelling capacity, and focusing on the practical development of energy system scenarios. MUSEPLAN draws on the specialist simulation model EnergyPLAN but provides an environment for integrated design and comparison of multiple scenarios while reducing the complexity through discarding some of the more advanced options. In conclusion, MUSEPLAN resolves the identified challenges to the integration of energy system modelling in municipal energy planning, while simplifying the modelling and scenario evaluation process.

One commonly-used argument against fluctuating renewables is their unpredictability. In contrast, thermal power generation and hydropower are regularly presented as reliable and dispatchable. However, droughts and floods can render useless the share of the power generation infrastructure that directly depends on freshwater. In this work, the global power sector is analysed from an energy-water nexus perspective to evaluate its reliability in case of severe water scarcity on a per-power plant basis, proposing a new method for combining it with water stress scores. At a country level, known individual thermal and hydropower plants are paired with regional water stress projections from 2020 to 2030 and their water source as a bottom-up approach to account for the capacities at risk and identify the points where water dependence could render a power system unreliable. The results show that, globally, about 65 % of generating capacities are directly freshwater-dependent. Moreover, the share of capacities placed in the low-resiliency group increases from 9 % of the total installed in 2020 to over 24 % in 2030 in all scenarios. The findings could help guide the development of the global power sector towards a less water-dependent system and accelerate the deployment of low water-demand power generation technologies.

Aligning prosumers' electricity consumption to the availability of self-generated electricity decreases CO₂ emissions and costs. Nudges are proposed as one behavioral intervention to orchestrate such changes. At the same time, fragmented findings in the literature make it challenging to identify suitable behavioral interventions for specific households and contexts - specifically for optimizing self-consumption. We test three sequentially applied interventions (feedback, benchmark, and default) delivered by digital tools in a field experiment with 111 German households with rooftop-photovoltaics. The experiment design with a control-group, baseline measurements, and high-frequency smart-meter-data allows us to examine the causal effects of each intervention for increasing self-consumption. While feedback and benchmark deliver small self-consumption increases (3–4 percent), the smart changing default leads to a 16 percent increase for active participants. In general, households with controllable electric vehicles show stronger effects than those without. For upscaling behavioral interventions for other prosumers, we recommend interventions that require little interaction and energy literacy because even the self-selected, motivated sample rarely interacted with the digital tools.

This paper presents a Reinforcement Learning (RL) approach to a price-based Demand Response (DR) program. The proposed framework manages a dynamic pricing scheme considering constraints from the supply and market side. Under these constraints, a DR Aggregator (DRA) is designed that takes advantage of a price generator function to establish a desirable power capacity through a coordination loop. Subsequently, a multi-agent system is suggested to exploit the flexibility potential of the residential sector to modify consumption patterns utilizing the relevant price policy. Specifically, electrical space heaters as flexible loads are employed to cope with the created policy by reducing energy costs while maintaining customers' comfort preferences. In addition, the developed mechanism is capable of dealing with deviations from the optimal consumption plan determined by residential agents at the beginning of the day. The DRA applies an RL method to handle such occurrences while maximizing its profits by adjusting the parameters of the price generator function at each iteration. A comparative study is also carried out for the proposed price-based DR and the RL-based DRA. The results demonstrate the efficiency of the suggested DR program to offer a power capacity that can maximize the profit of the aggregator and meet the needs of residential agents while preserving the constraints of the system.

The buildings energy consumption is a great part of Europe's overall energy demand. The development of diagnostic methods capable of promptly alerting users in case of issues (e.g. mild and progressive decrease in systems components performance) is crucial for the smart management of buildings. Machine learning-based building energy monitoring is a reliable approach for identifying subtle anomalies in the building energy demand behaviour. This study presents the application of a systematic procedure to develop a reliable monitoring method based on machine learning predictive models, ensuring minimal user knowledge requirements. The proposed method applied to the electricity demand of various components of the heating, ventilation and air conditioning system of a real Italian healthcare facility. The obtained models are exploited to apply the building energy monitoring method, assessing its capability to highlight mild changes in building energy demand behaviour. Considering that its application on specific system components implies an increased technical and economic effort to carry out data collection, the present work is aimed at assessing the benefits of such applications. Because of its high reproducibility and relatively simple integration into centralized building energy management systems, the proposed method offers a practical solution to enhance the smart management of building energy systems.

Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households’ electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Against the background of accelerating climate change, this paper examines to which extent sustainable infrastructure finance can effectively contribute to the European heat transition as a part of a “Great Transformation” towards a climate neutral economy and society. Since the building sector is responsible for approximately 35% of the EU's carbon footprint, district heating and cooling networks can provide an efficient technology for decarbonizing the energy supply of buildings. New district heating and cooling networks technology allows for heat and hot water generation that is combustion free. A large-scale role-out of this infrastructure would require hundreds of billions EUR of investments within the next few years. In view of the high public debt, the public sector will not be able to finance the required investment volumes. Against the background of regulatory changes, such as the EU Action Plan on Financing Sustainable Growth, this paper examines in which way financial markets participants might be able to fill the funding gap. A particular focus lies on blended finance, since related instruments reduce investors' risks, esp. in early stages of the infrastructure lifecycle. Due to an improved risk-return-relationship this makes the investment more attractive to private investors. It is also essential for investors to understand the kind of business model they invest in. Therefore, we discuss the importance of key performance indicators in the four dimensions that are relevant for the investors' decision-making process: return, risk, liquidity and sustainability. With respect to the sustainability dimension, we elaborate on the relevance of EU-Taxonomy-aligned district heating and cooling networks' construction and operation.

Buildings can deliver short-term thermal energy storage by utilising the thermal capacity of the building construction and/or by activating the water tanks included in the heating/cooling installation. The flexibility potential of demand management using decentralized thermal energy storage has been quantified in many theoretical modelling studies, and it is considered an essential technology for an affordable energy transition. We have investigated the drivers and barriers to the adoption of demand management in buildings in district heating and cooling systems via a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis and presented 17 elements that shape the current and future application of this concept. The results indicate that the application of the DR concept has left the theoretical studies and moved towards real-life applications. Yet, there is a lack of feasible business models and regulatory frameworks supporting the large-scale application of the concept. Utilities and their customers do not fully understand the benefits of the DR concept; therefore they are reluctant to adopt it outside of the research projects where the test environment is fully controlled and with limited impact and timeline. Therefore, the regulatory framework must be adjusted to allow DHC operators to develop new business models and DR tariffs that will incentivise the customers to deliver flexibility to the system without compromising their comfort and everyday practices and increasing energy poverty.

As the transition towards a net-zero gains momentum, smart local energy systems (SLES) will play a key role in delivering clean and sustainable energy in various forms of usage such as heat, electricity, and transportation, to communities where these projects are implemented. Successful SLES have previously shown a combination of cutting-edge engineering technology, as well as social and economic factors coming into play to achieve a set goal. The interdependencies between these contributing factors illustrates the multi-attribute nature of SLES. This article highlights how insightful models can be in upscaling SLESs. The models considered were categorized according to their modes of application, and instances where they have been used for modelling a multi-energy system upscale. Multi-criteria analysis was used to rank these models according to their ability to represent SLES. Four major aspects of upscaling (growth, replication, accumulation, and transformation) were used to weight the criteria using the entropy method and CRITIC method, respectively. The TOPSIS method was used to rank the models and the result indicated that among 21 models considered, the hybrid combination of an optimization model, a weighting model, and a multi-criteria decision model was the closest to the ideal solution.