Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

Abstract

As the local energy sources are mostly uncertain and fluctuating in nature, the ‘energy risk’ due to discrepancies between committed energy transactions and metered measurements is prominent in peer to peer (P2P) markets. We propose a P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers. The risk in energy production is modeled using Markowitz portfolio theory and the best point where energy return per unit risk is maximum is obtained from the efficient frontier by using the modified Sharpe ratio. The energy portfolio thus obtained is used as a constraint while optimizing the conflicting prosumer benefits using multi-objective stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC-NSPSO). In effect, only a reliable proportion of total energy demand submitted in the bid is cleared in the market, foreseeing the real-time fluctuations. The proposed market settlement mechanism also gives room to the existing distribution system operators by assigning them the duty of 1) optimally allocating energy among buyers and sellers in accordance with their competitive bids 2) providing the infrastructure, managing the market and charging for the service and 3) checking the technical feasibility by performing load flow and monitoring power transfer sensitivities to encourage short distance transactions. The energy allocation is done in CIGRE LV benchmark microgrid with ten peers having solar and wind generation. The allocated energy is found to be closer to the metered measurements and hence the reserve cost is observed to be low.

Transactive energy management systems: Mathematical models and formulations

“…The seller and buyer can share the network utilization charge (NUC) towards using the power network to transfer energy. In [68] and [69], the NUC was calculated using the power transfer distribution factor (PTDF) as: $$$$\begin{equation}\text{NUC}_{ij} = \mathop \sum \limits_{l = 1}^L \frac{{\text{PTDF}_{ij}^l \times {d}_l}}{{{P}_l}}\end{equation}$$$$$${{{d}}}_{{l}}$$ is the utilisation fee, and $${{{P}}}_{{l}}$$ is the total power flow in the line. In [70], the authors developed a model for the energy management of a residential microgrid with various houses, namely traditional, proactive, and enthusiastic.…”

“…The seller and buyer can share the network utilization charge (NUC) towards using the power network to transfer energy. In [68] and [69], the NUC was calculated using the power transfer distribution factor (PTDF) as:…”

New Methods for Improved Prosumer Trading and Energy Poverty Mitigation in Microgrids using Crowdsourcing Concepts and Blockchain Technologies

“…Recent studies have considered combining the operation of small-scale renewable energy sources (SSRES) in distribution networks and deregulated electricity markets. The range of these researches is covering unit commitment [19] and economic dispatch problems [20] in addition to scheduling of SSRES [21], and the uncertainty of renewable generation [22]. The main trends and approaches currently described in the literature are shortly summarized in the following paragraphs.…”

“…From the 27 consumers existing in the microgrid, the case study considers that only 11 are participating in the primary market as buyers (from buses 5, 8, 9,11,12,14,16,19,20,24,26), chosen mainly between the residences with high daily electricity demand. For each hour h, they can submit to the market two type of offers regarding the traded quantity: the entire hourly demand and forecasted values, multiple of 100 kW, as discussed in Section 3.2.…”

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

“…Financial risk assessment techniques are being increasingly used in the field of power systems to manage renewable energy risks in generation planning and electricity markets [23]. The Markowitz mean‐variance theory [24], value at risk (VaR) [25] and Sortino ratio [22] are examples of financial risk measurement tools that have been tested in power systems. The conditional value at risk (CVaR) is another measure which has performed better than the VaR.…”