Abstract

Renewable energy sources are variable and pose new challenges for power systems. A flexible energy management framework is needed for distributed energy resources (DERs) to improve
power system performance. Although home energy management systems (HEMSs) can control household appliances, they can not address the issues that may arise due to high DER penetration
levels on a distribution network. A multi-level energy management system (ML-EMS) is necessary to improve the techno-economic performance of the distribution system and satisfy the objectives
of end-users, aggregators, electricity retailers, and the distribution system operator (DSO). With the rise of DERs, consumers are progressively shifting towards the role of “prosumers,” serving as
flexible energy resources for DSOs. This work proposes a novel ML-EMS coordination framework in which prosumers provide upward and downward flexibility to the DSO. The DSO optimizes the
whole system with the optimal flexibility request sent to the aggregator. The suggested methodology considers the conflicting techno-economic objectives of the DSO and prosumers. To evaluate
the proposed method, we compare two scenarios: without flexibility and with flexibility provision. The results show that our proposed strategy improves the voltage profiles and reduces power losses,
power generation costs, and peak demands from the DSO’s perspective.

To motivate consumers to participate in the proposed coordination framework, an adaptive incentive program is proposed based on the flexibility of the end-user. The prosumer will receive
incentives to provide more flexibility to the DSO. To evaluate the proposed methodology, a comparative analysis is conducted involving five scenarios: ML-Framework (a) without HEMS (base
case), (b) without flexibility and an incentive program, (c) with flexibility and no incentive, (d)with flexibility and a fixed incentive, and (e) with flexibility and an adaptive incentive program.
The results show that our proposed strategy has increased the monetary benefits for prosumers for their flexibility services provided to the DSO compared to other scenarios. Moreover, the proposed
method improves the voltage profiles and reduces the peak load and power losses of a 33-bus radial distribution system.

Taking flexibility to the next level, we propose peer-to-peer (P2P) energy trading to buy and sell energy from neighbors using a smart transformer as an aggregator in our ML-EMS. This part
of the work presents a new coordination framework for HEMS-integrated P2P trading, focusing on the impact of such trading on a distribution transformer. The proposed framework provides a
comprehensive solution to manage power distribution within a smart grid environment by enabling HEMS to engage in P2P trading. This work also examines optimal energy management in a smart
neighborhood to minimize the total cost of energy usage. In addition, to prevent power peaks – that could create overloading and damage the top pole transformer, an adaptive cap within the flexibility
bound of the household is placed on the total power households that can draw/penetrate from/to the power grid. To validate the proposed method, we consider three scenarios: a) HEMS directly with
transformer. b) HEMS with integration of rule-based P2P with transformer, c). HEMS With fixed power limit on transformer. The result shows that the proposed method reduces the electricity cost
of the prosumers and extends the life expectancy of the transformer.

To include the three-phase unbalanced distribution system in the proposed framework, we develop another strategy, which includes four-stage optimization for a three-level coordination framework.
A mixed integer linear programming (MILP)-based HEMS is formulated in the first stage to perform home energy management effectively. At the aggregator level, in the second stage, a MILP-enabled P2P trading mechanism is designed. At the same level, a third-stage loss of life optimization is performed pertaining to the optimal power status of the HEMS and P2P trading. In the last stage, a three-phase optimal power flow-based optimization is proposed to maintain the operational constraints of the unbalanced distribution network. This work compares the proposed P2P-based method with a local energy market community with a HEMS-based smart home neighborhood with a distribution transformer. Optimizing HEMS and P2P trading while addressing transformer limitations, our proposed method reduces peak power and life loss of distribution transformers. Additionally, our method substantially lowers electricity costs for P2P prosumers. Thus, our proposed method outperforms other existing mechanisms from both financial and physical network operation suitability perspectives.