Smart Energy最新文献

In line with the Swedish target of carbon neutrality by 2045, the municipality of Växjö in Kronoberg County has set its own target to be carbon neutral in 2030. Currently, the Municipality's partially decentralized energy system relies heavily on interconnected electricity supply from the national grid, and fuels imports from other parts of Sweden. Under this circumstance, several concerns arise, including: in which ways future demand changes induce supply changes, and whether a future carbon-neutral energy system will be less costly in a sustained-electricity supply condition. In this study, techno-economic evaluations are conducted for different carbon-neutral scenarios for Växjö’s future energy system in 2030 and 2050, using an hour-by-hour dynamic energy simulation tool of EnergyPLAN. Projections for the future energy demands for Växjö were developed and modeled, based on the development strategies and on the national sustainable future scenarios in Sweden. Results for the Växjö’s carbon-neutral scenarios showed that the current energy system is sufficient to satisfy future heat demand. However, fulfilling demands of electricity for all sectors and fuels for transport and industry is a challenge. In the short term and at increased energy demand and price, being carbon neutral is technically viable without major changes in energy supply technologies. However, in the long term, investment for intermittent renewable energy resources, together with carbon capture and storage is considered to be viable financially. Therefore, planning for a carbon-neutral Växjö based on local investments showed to be a feasible strategy.

This paper presents the PEACEFULNESS software platform (Platform for transvErse evAluation of Control stratEgies For mULti-eNErgy Smart gridS), an open framework dedicated to multi-energy smart-grids, based on a techno-economic model that integrates economic considerations (contracts). As such, it is mainly oriented towards the evaluation of multi-energy grid supervision strategies, that is, energy management, and the corresponding policies and legal organization. The main goal is then to highlight the various possible behaviors and strategies to organize the probable future interconnections between the different energy carriers. In particular, it aims at investigating how to maximize the use of renewable energy sources (RES), using Demand Side Management (DSM) techniques and energy storage, in a shared economy context. The open-source tool PEACEFULNESS, written in Python, is described here in detail. It combines a top-down description of the energy networks and connections between the various agents (energy providers, distribution system operators, aggregators, consumers, producers, prosumers, etc.), together with a techno-economic bottom-up description for all devices. Here, both public databases and users’ data (basic heating demands or based on building modeling) can be used, as well as generic or more specific models (e.g., PV panels with constant or temperature-dependent efficiency). One of its major unique features compared with other tools is that it extends the use of DSM techniques to various energy grids which can also interact together. Furthermore, different economic models can be set for both the aggregators and the customers, and even within these groups. As a last competitive advantage, PEACEFULNESS allows the user to simulate the operation and supervision of tens up to hundreds of thousands of agents. It also provides a reporting system giving access to all the data, with a configurable granularity and frequency for the retained indicators. Finally, several validation cases are presented, followed by a series of test cases with increasing size: a smart home, a smart district (2 000 dwellings) and a smart community (50 000 dwellings).

In June 2018, an ambitious target has been set in Austria for the domestic expansion of electricity generation from renewable energy sources (RES) by the Austrian Climate and Energy Strategy: The goal is to cover the total national electricity consumption, measured by yearly balance, with RES. With this goal, the country's power system is facing a significant transformation. Not only the necessary expansion rate of RES, but also safeguarding system stability, and preserving security of supply are major challenges that need to be tackled in the coming years. A high share of electricity generation from hydro, wind and PV is expected to lead to considerable, weather-related fluctuations in power supply. System flexibility is required to compensate short-to long-term (seasonal) differences between generation and consumption. This paper aims for assessing short-to long-term flexibility needs of the Austrian electricity system by 2030, which is intended to rely, almost exclusively, on RES and the use of flexibility options for meeting those needs. For this purpose, a high-resolution power and district heating model is used for the calculation of two distinct scenarios. Flexibility needs and coverage are quantified for these scenarios for different timescales, namely: daily, weekly, monthly and annually.

District heating (DH) widespread in Estonia provides at a national level the most efficient opportunity to achieve the objectives of primary energy efficiency, increasing the percentage of renewable energy and reducing the CO₂ emissions.

All previous calculations of CO₂ intensity of Estonian DH were performed only based on statistics published by Eurostat and Statistics Estonia. To increase accuracy of initial data and receive results for specific DH networks the real operational data of main Estonian DH networks were collect for year 2020.

Calculations are performed using power bonus calculation method and proportional distribution method. Special attention is paid on CO₂ emission factors of different fuels and energy inputs to the DH systems. One of the main issues discussed is definition of waste heat and applicability of CO₂ emission factors.

Depending on the methodology used to calculate the weighted average CO₂ emission factor for DH sector, the factor is either −19.8 kgCO₂/MWh_heat (‘power bonus’ method) or 85.6 kgCO₂/MWh_heat (proportional distribution).

Revised and clarified main steps for calculation of DH CO₂ emission factors presented in this article assumed to be used by the members of Estonian Power and Heat Association. Common calculation approach will allow transparent benchmarking of Estonian DH systems and can be used as a good way to inform DH consumers about the CO₂ intensity and sustainability of specific DH networks as well as DH sector in general.

Nowadays, nearly one quarter of global carbon dioxide emissions are attributable to energy use in industry, making this an important target for emission reductions. The scope of this study is hence that to define a cost-optimized decarbonization strategy for an energy and carbon intensive industry using an Italian refinery as a case study. The methodology involves the coupling of EnergyPLAN with a Multi-Objective Evolutionary Algorithm (MOEA), considering the minimization of annual cost and CO₂ emissions as two potentially conflicting objectives and the energy technologies’ capacities as decision variables. For the target year 2025, EnergyPLAN + MOEA has allowed to model a range of 0–100% decarbonization solutions characterized by optimal penetration mix of 22 technologies in the electrical, thermal, hydrogen feedstock and transport demand. A set of nine scenarios, with different land use availabilities and implementable technologies, each consisting of 100 optimal systems out of 10,000 simulated ones, has been evaluated. The results show, on the one hand the possibility of achieving medium-high decarbonization solutions at costs close to current ones, on the other, how the decarbonization pathways strongly depend on the available land for solar thermal, photovoltaic and wind, as well as the presence of a biomass supply chain in the region.

Dynamic pricing is designed for the load shaping to help match the amount of the energy demand to the energy supply capacity. Since the buildings’ characteristics influence the performance of the energy shifting, renovation of the building towards a higher energy flexibility is worth investigating. This study evaluated the effect of the envelope on building thermal storage performance. A model predictive control (MPC) was developed to achieve a multi-objective control i.e., indoor comfort temperature and minimise the total energy cost. MPC automatically triggered the energy storage during the low price periods and used the stored energy during the high price periods. The results confirmed the ability of MPC on peak demand reduction up to 45% electricity cost. Besides, the results also demonstrated the ability of heavyweight thermal mass in terms of reducing energy consumption and shifting a greater high price energy to the low price times. Therefore, adding insulation layers into the lightweight thermal mass is highly recommended, especially for the places experiencing the significant mismatch between the demand and supply during daily peaks or the areas scheduling a large amount of intermittent renewable energy source in the energy production.

Grid decarbonization efforts can benefit significantly from demand response (DR) resources. However, system-level changes that affect the net-load such as increased variable renewable energy (VRE) generation and widespread deployment of energy efficiency (EE) also affect the type, magnitude and timing of DR required to support the grid. In this study, we use publicly available system-level data to define seven metrics to assess how these changes affect system-level shed and shift DR needs. Specifically, there are four metrics for grid conditions when DR has the highest system value and three metrics for DR program design that were developed by considering the magnitude and temporal distribution of net-load. We also develop three stylized load shape profiles illustrating EE measure impacts and one high VRE generation profile to demonstrate the application of these metrics. The results confirm the robustness of the metrics to identify complex interactions between demand-side and supply-side resources that can affect the DR need. Widespread application of our metrics can help system planners and operators be cognizant of such interactions and identify the DR need for the system in a way that can be most valuable.

Hydrogen is commonly mentioned as a future proof energy carrier. Hydrogen supporters advocate for repurposing existing natural gas grids for a sustainable hydrogen supply. While the long-term vision of the hydrogen community is green hydrogen the community acknowledges that in the short term it will be to large extent manufactured from natural gas, but in a decarbonized way, giving it the name blue hydrogen. While hydrogen has a role to play in hard to decarbonize sectors its role for building heating demands is doubtful, as mature and more energy efficient alternatives exist. As building heat supply infrastructures built today will operate for the decades to come it is of highest importance to ensure that the most efficient and sustainable infrastructures are chosen. This paper compares the source to sink efficiencies of hydrogen-based heat supply system to a district heating system operating on the same primary energy source. The results show that a natural gas-based district heating could be 267% more efficient, and consequently have significantly lower global warming potential, than a blue hydrogen-based heat supply A renewable power-based district heating could achieve above 440% higher efficiency than green hydrogen-based heat supply system.

This article compares the robustness of the optimal choice of technologies for two Smart Energy Systems architectures at district level, illustrated by a case study representative of a newly built district in Grenoble, France. The electricity-driven architecture relies on the national electric grid, decentralized photovoltaic panels and decentralized heat pumps for heat production building by building. The alternative architecture consists of a district heating network with multiple sources and equipment for centralized production of heat. Those are a gas boiler plant, a biomass-driven cogeneration plant, a solar thermal collector field, and a geothermal heat pumping plant (grid-driven or photovoltaics-driven). Electric and heat storages are considered in both architectures. The sizing and operation of both architectures are optimized via linear programming, through a multi-objective approach (total project cost versus carbon dioxide emissions). Both architectures are compared at nominal scenario and at sensitivity scenarios. It is concluded that the electricity-driven architecture is less robust, especially to uncertainties in space heating demands (+150%/−30% impact on costs) and in heat pump performance (+35%/−15% in costs). Meanwhile, the multi-source architecture is less sensitive to space heating demands (+110%/−30%) and has negligible sensitivity to the rest of parameters except photovoltaic panels efficiency (+14%/−7%).

District heating (DH) networks have the potential for intelligent integration and combination of renewable energy sources, waste heat, thermal energy storage, heat consumers, and coupling with other sectors. As cities and municipalities grow, so do the corresponding networks. This growth of district heating networks introduces the possibility of interconnecting them with neighbouring networks. Interconnecting formerly separated DH networks can result in many advantages concerning flexibility, overall efficiency, the share of renewable sources, and security of supply. Apart from the problem of hydraulically connecting the networks, the main challenge of interconnected DH systems is the coordination of multiple feed-in points. It can be faced with control concepts for the overall DH system which define optimal operation strategies. This paper presents two control approaches for interconnected DH networks that optimize the supply as well as the demand side to reduce CO₂ emissions. On the supply side, an optimization-based energy management system defines operation strategies based on demand forecasts. On the demand side, the operation of consumer substations is influenced in favour of the supply using demand side management. The proposed approaches were tested both in simulation and in a real implementation on the DH network of Leibnitz, Austria. First results show a promising reduction of CO₂ emissions by 35% and a fuel cost reduction of 7% due to better utilization of the production capacities of the overall DH system.