Chapter 22 - Storage Solutions and Their Value

The current focus on the massive CO2 reduction highlights the need for the rapid development of technology for the production, storage, transportation and distribution of renewable energy. In addition to electricity, we need other forms of energy carriers that are more suitable for energy storage and transportation. Hydrogen is one of the main candidates for this purpose, since it can be produced from solar or wind energy and then stored; once needed, it can be converted back to electricity using fuel cells. Another important aspect of future energy systems is sector coupling, where different sectors, e.g. mobility and energy, work together to provide better services. In such an integrated system, electric vehicles – both battery and hydrogen-based fuel cell – can provide, when parked, electricity services, such as backup power and balancing; when driving they produce no emissions. In this paper we present the concept design and energy management of such an integrated energy and mobility system in a real-life environment at the Shell Technology Centre in Amsterdam. Our results show that storage using hydrogen and salt caverns is much cheaper than using large battery storage systems. We also show that the integration of electric vehicles into the electricity network is technically and economically feasible and that they can provide a flexible energy buffer. Ultimately, the results of this study show that using both electricity and hydrogen as energy carriers can create a more flexible, reliable and cheaper energy system at an office building.

Development of the effective defrosting criterion for electric vehicles transcritical CO2 heat pumps under constant heating capacity operation

Exploring the willingness of consumers to electrify their homes

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm.

This study proposes a distribution-level Energy System Integration and Aggregation (ESIA) model to evaluate the flexibility value of electric vehicles (EVs) and heat pump water heaters (HPWHs) under distribution transformer constraints. The model optimizes their operation based on household demand, photovoltaic generation, and mobility patterns.Using Japan’s FY2022 spot prices, simulation results show that day-ahead market optimization significantly reduces electricity procurement costs. Anticipated congestion-based control further mitigates reverse power flow with minimal cost increase, especially for HPWHs and EV charging. However, EV discharging faces opportunity loss due to reduced arbitrage flexibility during reserved periods.

The aim of this paper is to compare energy consumptions, CO2 emissions, and operative costs of condensing boilers, electric vapour compression heat pumps and gas driven absorption heat pumps to provide space heating and domestic hot water. The analysis is performed for 140 m2 single-family houses in five different Italian cities whose envelope features depend on the location. For each location, two different envelope conditions are considered. The first one is a non-insulated building, while the second one is the same building, but an external thermal insulation is added on vertical walls and roof. To avoid internal renovation, radiators are maintained as emission system. Combined dynamic simulations are performed to appreciate building and system interactions. A 6 second time step is set to evaluate properly interactions and the DHW profile demand. In addition, the GHP dynamic model is a grey box model experimentally validated. The results show that electric vapour compression heat pumps reach the highest non-renewable primary energy savings (>32%) compared to condensing boilers, but their operative costs are higher due to the higher specific cost of electricity in Italy. Gas driven absorption heat pumps achieve a lower consumption reduction than electric heat pumps (>22%), but they have also the minimum operative cost among the three technologies.

The design and analysis of two exhaust heat recovery systems for public shower facilities

Approximately one quarter of energy-related emissions in Germany are caused by the domestic sector. At 81%, the largest share of these emissions is due to heat supply. Many measures are available to reduce these emissions. One of these measures, which is considered to play an important role in many studies, is the replacement of fossil-fired boilers with electric heat pumps. In order to be able to analyse the impact of high penetrations of heat pumps on the energy system, the coefficient of performance (COP) must be modelled with high temporal resolution. In this study, a methodology is presented on how to calculate high-resolution COPs and the electrical load of heat pumps based on thermal load curves and temperature time series. The COP is determined by the reciprocal Carnot factor. Since heat pumps are often designed bivalently due to the cost structure, the methodology described can also be used for evaluating the combination of immersion heater and heat pump (here for the air/water heat pump). As a result the theoretical hourly COPs determined are calibrated with annual performance factors from field tests. The modelled COPs show clear differences. Currently, mostly air source heat pumps are installed in Germany. If this trend continues, the maximum electrical load of the heat supply will increase more than would be the case with higher shares of ground source heat pumps.

Integration of PV power into future low-carbon smart electricity systems with EV and HP in Kansai Area, Japan

The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur.

The decarbonization of the energy system will bring substantial changes, from supranational regions to residential sites. This review investigates sustainable energy supply, applying a multi-sectoral approach from a residential site perspective, especially with focus on identifying crucial, plausible factors and their influence on the operation of the system. The traditionally separated mobility, heat, and electricity sectors are examined in more detail with regard to their decarbonization approaches. For every sector, available technologies, demand, and future perspectives are described. Furthermore, the benefits of cross-sectoral integration and technology coupling are examined, besides challenges to the electricity grid due to upcoming technologies, such as electric vehicles and heat pumps. Measures such as transport mode shift and improving building insulation can reduce the demand in their respective sector, although their impact remains uncertain. Moreover, flexibility measures such as Power to X or vehicle to grid couple the electricity sector to other sectors such as the mobility and heat sectors. Based on these findings, a morphological analysis is conducted. A morphological box is presented to summarize the major characteristics of the future residential energy system and investigate mutually incompatible pairs of factors. Lastly, the scenario space is further analyzed in terms of annual energy demand for a district.

<p>As nations set ambitious targets to phase out internal combustion engine (ICE) vehicles and electrify transport and household systems, it is anticipated that there will be a surge in demand for electric vehicles (EVs), heat pumps (HPs), and electric water heaters (EWHs). Utility providers must assess what this shift means for the existing grid. Although generation may be sufficient, attention must turn to local distribution, particularly individual street- level transformers. As EV chargers are installed and households replace gas systems with HPs and EWHs, the grid faces rising pressure. Many consumers prefer to charge vehicles at home in the evening, placing extra demand on local infrastructure. When several residents charge EVs while using water heating and cooling systems, transformer capacity may be exceeded. This study uses data from an Ontario utility to examine new loading scenarios as EVs, HPs, and EWHs are adopted in Canadian neighbourhoods. One transformer showed a maximum hourly load increase of 706.59% at 25% uptake and 1292.22% at 50%. Sustained high loads accelerate insulation ageing and raise the risk of early failure<strong>, </strong>with overloads possible when 11 EVs charge simultaneously at high speed.</p>

The decarburization of heat and transport using electric vehicles (EVs) and heat pumps (HPs) needs an important investment in low-voltage (LV) networks. This article proposes an optimization approach to estimate the availability of headroom for domestic EV charging and optimization on LV networks under various penetrations of heat pumps. The proposed optimization approach is called dynamic differential annealed optimization (DDAO). The objective of the proposed approach is to maximize the hosting capability of low-voltage networks for EVs and HPs by optimizing the flexibility of EV charging. The DDAO is used to optimize different aspects of EV charging and LV networks. The DDAO approach is implemented in three steps: (a) HP headroom assessment, (b) EV optimization, and (c) validation. The performance of the proposed method is validated on a MATLAB platform and compared with the existing Binary Spring Search Algorithm (BSSA), Manta Ray Foraging Optimization Algorithm (MRFO), and Chaos Game Optimization methods. In comparison to the total prices of 3.5£ in BSSA, 4.5£ in MRFO, and 5.5£ in CGO, the proposed DDAO method achieves a lower total price of 3.2£. This suggests that the DDAO method is more cost-effective than the existing techniques.

Experimental evaluation of electric vehicle compressor lubricating oil robustness for heat pump application

External costs and taxes in heat supply systems