One of the issues concerning the replacement of natural gas by green gas is the seasonal pattern of the gas demand. When constant production is assumed, this may limit the injected quantity of green gas into a gas grid to the level of the minimum gas demand in summer. A procedure was proposed to increase thegas demand coverage in a geographical region, i.e., the extent to which natural gas demand is replaced by green gas. This was done by modeling flexibility into farm-scale green gas supply chains. The procedure comprises two steps. In the first step, the types and number of green gas production units are determined,based on a desired gas demand coverage. The production types comprise time-varying biogas production, non-continuous biogas production (only in winter periods with each digester having a specified production time) and constant production including seasonal gas storage. In the second step locations of production units and injection stations are calculated, using mixed integer linear programming with cost price minimization being the objective. Five scenarios were defined with increasing gas demand coverage, representing a possible future development in natural gas replacement. The results show that production locations differ for each scenario, but are connected to a selection of injection stations, at least in the considered geographical region under the assumed preconditions. The cost price is mainly determined by the type of digesters needed. Increasing gas demand coverage does not necessarily mean a much higher cost price.
Decentralised renewable energy production in the form of fuels or electricity can have large scale deployment in future energy systems, but the feasibility needs to be assessed. The novelty of this paper is in the design and implementation of a mixed integer linear programming optimisation model to minimise the net present cost of decentralised hydrogen production for different energy demands on neighbourhood urban scale, while simultaneously adhering to European Union targets on greenhouse gas emission reductions. The energy system configurations optimised were assumed to possibly consist of a variable number or size of wind turbines, solar photovoltaics, grey grid electricity usage, battery storage, electrolyser, and hydrogen storage. The demands served are hydrogen for heating and mobility, and electricity for the households. A hydrogen residential heating project currently being developed in Hoogeveen, The Netherlands, served as a case study. Six scenarios were compared, each taking one or multiple energy demand services into question. For each scenario the levelised cost of hydrogen was calculated. The lowest levelised cost of hydrogen was found for the combined heating and mobility scenario: 8.36 €/kg for heating and 9.83 €/kg for mobility. The results support potential cost reductions of combined demand patterns of different energy services. A sensitivity analysis showed a strong influence of electrolyser efficiency, wind turbine parameters, and emission reduction factor on levelised cost. Wind energy was strongly preferred because of the lower cost and the low greenhouse gas emissions, compared to solar photovoltaics and grid electricity. Increasing electrolyser efficiency and greenhouse gas emission reduction of the used technologies deserve further research.
In the coming decades, a substantial number of electric vehicle (EV) chargers need to be installed. The Dutch Climate Accord, accordingly, urges for preparation of regional-scale spatial programs with focus on transport infrastructure for three major metropolitan regions among them Amsterdam Metropolitan Area (AMA). Spatial allocation of EV chargers could be approached at two different spatial scales. At the metropolitan scale, given the inter-regional flow of cars, the EV chargers of one neighbourhood could serve visitors from other neighbourhoods during days. At the neighbourhood scale, EV chargers need to be allocated as close as possible to electricity substations, and within a walkable distance from the final destination of EV drivers during days and nights, i.e. amenities, jobs, and dwellings. This study aims to bridge the gap in the previous studies, that is dealing with only of the two scales, by conducting a two-phase study on EV infrastructure. At the first phase of the study, the necessary number of new EV chargers in 353 4-digit postcodes of AMA will be calculated. On the basis of the findings of the Phase 1, as a case study, EV chargers will be allocated at the candidate street parking locations in the Amsterdam West borough. The methods of the study are Mixed-integer nonlinear programming, accessibility and street pattern analysis. The study will be conducted on the basis of data of regional scale travel behaviour survey and the location of dwellings, existing chargers, jobs, amenities, and electricity substations.
