Optimally allocating renewable generation in Ireland: a long-term outlook until 2050. ESRI Research Bulletin, 2018/03

The Irish energy white paper released in December 2015 states the objective of diversifying electricity generation from renewable energy sources (RES-E). While onshore wind is planned to continue to make a significant contribution, the question arises which roles other RES-E technologies, such as solar PV, wind offshore or bioenergy, will play in the future. Moreover, the Irish 2030 target for RES-E is about to be set. Since the electricity demand growth in future is uncertain and the national target is yet unknown, this creates a high uncertainty around the overall amount of RES-E required. In this uncertain context, this research seeks to provide support for 1. achieving the national RES-E target determined as percentage share of energy demand in a cost minimal way under consideration of different diversification approaches, and 2. long-term planning of the electricity system by providing insight into the future regional distribution of generation and demand under different scenarios

Onshore wind energy in Baden-Württemberg: a bottom-up economic assessment of the socio-technical potential

Detailed information about the potentials and costs of renewable energies is an important input factor for energy system models, as well as commercial and political decision-making processes. With its increasing locally installed capacity and hub height, wind energy plays an important role when it comes to meeting climate targets and optimizing electricity networks. Recently however, wind energy has faced more and more social barriers and land use constraints which can negatively impact both political goals and investment decisions. Therefore this work presents a bottom-up methodology based on graph-theoretical considerations to account for social barriers to estimate the socio-technical potential and the associated costs on a wind farm level. Calculations are conducted for the German federal state of Baden-Württemberg as a case study and are based on high resolution land use and wind speed data, using an algorithm to place wind parks by considering further constraints relating to land use planning. The socio-technical potential is found to be less than half that of previous studies that neglect these constraints, i.e. between 11.8 and 29.4 TWh, with costs between 7 and 14 €ct/kWh. A sensitivity analysis reveals a strong dependency of the overall socio-technical potential as well as its distribution across the federal state. In order to test the quality of the algorithm, already existing and planned wind parks were compared to modeled wind park locations and a very good correlation could be observed. The focus in future work should lie on the development of an economic criterion, which unlike the LCOE is able to account for the system costs of a widespread wind energy development, including network expansion, balancing power and reserve energy costs

Demand response through decentralized optimization in residential areas with wind and photovoltaics

A paradigm shift has to be realized in future energy systems with high shares of renewable energy sources. The electrical demand has to react to the fluctuating electricity generation of renewable energy sources. To this end, flexible electrical loads like electric heating devices coupled with thermal storage or electric vehicles are necessary in combination with optimization approaches. In this paper, we develop a novel privacy-preserving approach for decentralized optimization to exploit load flexibility. This approach, which is based on a set of schedules, is referred to as SEPACO-IDA. The results show that our developed algorithm outperforms the other approaches for scheduling based decentralized optimization found in the literature. Furthermore, this paper clearly illustrates the suboptimal results for uncoordinated decentralized optimization and thus the strong need for coordination approaches. Another contribution of this paper is the development and evaluation of two methods for distributing a central wind power profile to the local optimization problem of distributed agents (Equal Distribution and Score-Rank-Proportional Distribution). These wind profile assignment methods are combined with different decentralized optimization approaches. The results reveal the dependency of the best wind profile assignment method on the used decentralized optimization approach

On the Role of Electricity Storage in Capacity Remuneration Mechanisms

In electricity markets around the world, the substantial increase of intermittent renewable electricity generation has intensified concerns about generation adequacy, ultimately driving the implementation of capacity remuneration mechanisms. Although formally technology-neutral, substantial barriers often exist in these mechanisms for non-conventional capacity such as electricity storage. In this article, we provide a rigorous theoretical discussion on design parameters and show that the concrete design of a capacity remuneration mechanism always creates a bias towards one technology or the other. In particular, we can identify the bundling of capacity auctions with call options and the definition of the storage capacity credit as essential drivers affecting the future technology mix as well as generation adequacy. In order to illustrate and confirm our theoretical findings, we apply an agent-based electricity market model and run a number of simulations. Our results show that electricity storage has a capacity value and should therefore be allowed to participate in any capacity remuneration mechanism. Moreover, we find the implementation of a capacity remuneration mechanism with call options and a strike price to increase the competitiveness of storages against conventional power plants. However, determining the amount of firm capacity an electricity storage unit can provide remains a challenging task

Energy autonomy in residential buildings: a techno-economic modelbased analysis of the scale effects

An increasingly decentralized energy supply structure alongside economic incentives for increasing the level of self-generation and –consumption are encouraging (higher levels of) energy autonomy. Previous work in this area has focused on the technical and economic aspects of energy autonomy at distinct scales, from individual buildings, through neighbourhoods to districts. This paper employs a mixed integer linear program (MILP) to assess the effects of aggregation across these scales on the economics of energy autonomy in residential buildings. The model minimizes total energy system costs over the lifetime of the energy system, including micro-CHP, PV, thermal and electrical storage, and boilers, at five distinct scales and for nine demand cases. It is subject to several constraints, amongst other things the degree of electrical self-sufficiency. The results indicate a shift in the economically optimal level of electrical self-sufficiency with scale, which in Single Family Households (SFHs) means from around 30% at the individual building level to almost 100% in districts of 1000 SFH households. Above around 560 households it could be economically advantageous to make a district of residential buildings electrically self-sufficient. In addition, a marginal increase in electrical selfsufficiency is significantly more expensive at lower aggregation scales (i.e. single buildings) compared to the scale of neighbourhoods and districts. The level of interaction with the electrical distribution network increases with increasing electrical self-sufficiency before then decreasing at very high (above 70%) levels. Future work should focus on a richer socioeconomic differentiation, considering other sectors and technologies, incorporating demand side options and analysing the effects on the overarching energy system

Development of a multi-energy residential service demand model for evaluation of prosumers’ effects on current and future residential load profiles for heat and electricity

The motivation of this thesis is to develop a multi-energy residential service demand (MESD) model. The approach is based on earlier modelling concepts. Electricity is simu- lated by the help of a first-order Markov-chain approach simulating pseudorandom solar irradiation data as well as occupancy patterns, which are matched to stochastically deter- mined electric appliance activities (McKenna et al., 2015; Richardson & Thomson, 2012). A lumped-parameter model simulating indoor temperatures is utilized to estimate space heating (SH) demand (Nielsen, 2005). Measurement data on domestic hot water (DHW) consumption in dwellings is analysed in order to implement a DHW model. The model generates output in 1-minute resolution. It features various possibilities of dwelling customization: Among others, number of residents, building physics, electric appliances and heating regime may be adjusted. An interface providing a link to the Cambridge Housing Model (DECC, 2012) is implemented, which supports automated re- trieval of relevant building parameters. Electricity and DHW demand values may also be extracted to be used for model calibration. The added value of this work is the implementation of a DHW model and the combination of above named approaches to an integrated multi-energy service demand model. The electricity model is enhanced by improving the calibration mechanism and increasing electric appliance variety. The SH model is extended by random heating regime genera- tion based on field data. The model features full year simulations incorporating seasonal effects on DHW and SH demand. In addition, seven representative archetypes have been developed, which allow for detailed investigation of load profiles for heat and electricity of representative UK dwellings. The model has a wide scope of application. It can be used to explore the impact of differ- ent dwelling configurations on load matching and grid interaction throughout the seasons. Synthetic energy service demand profiles may support research on the optimal configura- tion of on-site supply appliances such as mCHP, PV and heat pumps. Furthermore, the model allows for drawing conclusions on the net carbon emissions of a dwelling and for assessing energy-efficiency measures