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This paper investigates conversion of a Nordic oil-heated townhouse into carbon-neutral by different energy efficiency (EE) improvements and an off-grid system including solar photovoltaics (PV), wind power, and battery and hydrogen energy storage systems (BESS and HESS). A heat-pump-based heating system including waste heat recovery (WHR) from the HESS and an off-grid electrical system are dimensioned for the building by applying models developed in MATLAB and Microsoft Excel to study the life cycle costs (LCC). The work uses a measured electrical load profile, and the heat generation of the new heating system and the power generation are simulated by commercial software. It is shown that the EE improvements and WHR from the HESS have a positive effect on the dimensioning of the off-grid system, and the LCC can be reduced by up to €2 million. With the EE improvements and WHR, the component dimensioning can be reduced by 22%–41% and 13%–51% on average, respectively. WHR can cover up to 57% of the building's annual heat demand, and full-power dimensioning of the heat pump is not reasonable when WHR is applied. Wind power was found to be very relevant in the Nordic conditions, reducing the LCC by 32%. [Display omitted] • Energy efficiency improvements notably reduce dimensioning of system components. • Waste heat from FC and electrolyzer covers up to 57% of the annual heat demand. • A full-power-dimensioned GSHP does not reduce the fuel cell dimensioning. • Seasonal storage on a small scale makes the system not economically viable. • Small-scale wind power is expensive, but it significantly reduces the system LCC. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

Domestic hot water (DHW) heating is one of the most energy-consuming activities in a typical household. Photovoltaics (PV) connected with a ground source heat pump (GSHP) offers a low-emission method for DHW heating. This paper studies four different control methods for DHW heating in a building with a GSHP and a PV system. The main control method aims to minimize DHW heating costs by utilizing Nord Pool Spot market information together with a PV production output forecast. The results of this control method, implemented with a perfect PV output forecast and assessed over three years of hourly data, indicate that annual cost savings over other methods are achievable. Results with the real-world actual PV output forecast, evaluated between June–September 2020, demonstrate DHW heating cost savings up to 36–53%, even though forecasting errors are present. The heating costs are 9–11% higher compared against the perfect forecast case. The suggested control method thereby effectively reduces the costs when compared with all other methods, and its performance is not significantly affected even when an actual imperfect forecast is implemented. The results also indicate that minimizing energy consumption does not offer the lowest cost. • A solar PV production forecast is verified with measurements in a Nordic context. • A Cost optimal control method for domestic hot water heating is developed. • The control logic is based on PV forecast, electricity price, and COP curve data. • Clock control has 15–21% higher energy costs than the cost optimal control. [ABSTRACT FROM AUTHOR]

A modular power-hardware-in-loop (PHIL) simulator for water electrolyzers is developed to study the electrolyzer as part of a smart grid and to examine the characteristics of various electrolyzer power supply electronics. A PHIL simulator up to 405 A of continuous current is built using commercially available power electronic converters. The design and operating principle of the emulator are analyzed. The experimental dynamics tests are performed to verify the PHIL simulator operation and suitability for water electrolysis emulation considering the effect of supply power quality. Finally, the PEM stack model is implemented, and the PHIL simulator is used to emulate a commercial PEM electrolyzer following the measured solar photovoltaic (PV) output power. [ABSTRACT FROM AUTHOR]