Another recent thesis from the Department of Power Engineering at NTNU investigates the impacts of establishing a local peer-to-peer market in local energy communities (LECs). It analyzes these impacts based on voltage variations, grid dependency, and losses. The model in this thesis is based on two existing research models, one for multi-stage market optimization and one for power flow analysis. The thesis author modifies these models, and automizes the interaction between the models. The objective functions minimizes the electricity costs for the community 24 hours at a time, while conducting a full Alternating Current (AC) power flow analysis for each time step with this period, using a combination of Excel and MATLAB.

The P2P model used in this thesis is a multi-period linear programming model, based on the work of Lüth et al. [1], with some modifications made to fit its scope. The load flow analysis was performed with open-source tool MAT-POWER. The data sets for the analysis were provided by Zaferanlouei et al. [2]. It analyzed two different scenarios: one in which PV panels were built, and one in which batteries were installed in the community.

This thesis found that, PV production without batteries increases self-consumption, while significantly lowering the losses (44.7%) from grid import. Batteries also lower the losses, but there is a significant difference between the no market and P2P scenario for batteries. It found that introducing a P2P market increases losses by 13.8%, because nodes with batteries will often sell stored energy locally rather than using it for self-consumption.

The thesis found that while DERs and storage options significantly reduce cost to the community (30.71-34.10%), the savings of establishing a P2P market were much lower (2.63-3.45%). This was found to be in contrast to the results of Lüth et al. [1], where an implementation of local trade (without a power flow analysis) yielded savings of 16-22%. This difference most likely arises from the difference between Norwegian and UK power markets, as well as the existence of wind turbines in the UK case (which have complementary load profiles to solar PV).

The power flow analysis found some insignificant effects to the voltage levels in the cases studied. The integration of PVs decreased the grid demand by 13.73% while the integration of batteries decreased it by 19.19%, although the existence of a P2P market was found to have no effect. This peak demand increase could be curbed with a peak power term to decrease the profitability of extensive battery charging from grid import.

While the results of this thesis are likely high system-dependent, both from a market design and system setup perspective, it adds to the wealth of studies demonstrating the benefits of DER integration and P2P markets.

[1] A. Lüth, J. M. Zepter, P. C. del Granado, and R. Egging, “Local electricity market designs for peer-to-peer trading: The role of battery flexibility,” Applied Energy, vol. 229, pp. 1233 – 1243, 2018.

[2] S. Zaferanlouei, M. Korp°as, H. Farahmand, and V. V. Vadlamudi, “Integration of PEV and PV in Norway using multi-period ACOPF—Case study,” in 2017 IEEE Manchester PowerTech, pp. 1–6, 2017.

A recent thesis from the Department of Industrial Economics and Technology at NTNU investigates two different optimization-based system control strategies on a local electricity market level. These strategies are evaluated based on two outputs: the total cost of electricity during operation [for both the energy sharing region (ESR) as a whole and for each end-user within the ESR] and peak grid power demand, which is determined by the rate of self-consumption of distributed energy resources (DERs) within the ESR. To investigate the relationship between peak power demand and total electricity costs, a multi-objective optimization (MOO) approach based on the ϵ-constraint method was also implemented.

The first optimization strategy was the decentralized control system strategy, which minimized electricity costs for each end-user within the ESR, assuming they could only utilize their own local production, storage units, and the grid to meet their demands. The second optimization strategy, contrarily, minimizes electricity costs for the ESR as a whole, and enables peer-to-peer (P2P) trading amongst end-users. These strategies were applied to two different communities – 25 residential buildings in London, UK, and three large industrial end-users at Forus, Norway. The figure below illustrates the differences between the two strategies.

The results of the thesis show that the centralized optimization strategy with P2P electricity trading gave the lowest total costs for the ESR. A cost reduction of 1.0-8.0% was found when compared to the decentralized strategy, as well as a reduction of grid energy consumed of 1.4-18.9%. Finally, the results from the MOO show that there is a dependency between total electricity costs and peak power demand for the cases studied and that a small increase in cost can reduce the peak power demand by a significant amount.