The integration of power electronic converter (PEC)-based renewable distributed energy resources (DERs) and controllable loads in electric power grids is growing exponentially due to increased interest in reducing carbon emissions. However, the uncertainty in DER power output limits their contribution to the overall system reliability and alleviates its flexibility. Hence, three-stage power flow and flexibility reserve optimization are proposed to address these issues. In the first stage, the epistemic and aleatory uncertainties in the power flow problem are addressed by the proposed parameterized deterministic lookahead (DLA) method. The flexibility metric “variation in generation capacity (VIGC)” and sharing alternating direction method of multipliers (S-ADMM) based distributed optimization is used to schedule flexibility reserves and minimize its cost in the second and the third stage to enhance system flexibility. The flexibility reserve sharing and blockchain/smart contract-based bidding to schedule those reserves for multiple balancing authority (BA) areas are also proposed in this work. The proposed algorithm’s results and e?ectiveness are presented in Part II of this work.

In Part I of this work, we presented a three-stage framework for solving stochastic non-convex optimal power flow problems using a parameterized deterministic look-ahead policy and minimizing the cost of scheduling flexibility reserves using blockchain and smart contracts. Here, in Part II, a case study with two balancing authority (BA) areas is presented to show the effectiveness of the proposed algorithm. The BA area is modeled by the collection of generation, transmission, and distribution networks. Next, the reserve sharing strategy between two or more BA areas is presented to collectively maintain and supply the flexibility reserves. The reserve sharing will help reduce the total flexibility reserve scheduling costs in the BA areas. And finally, the advantage of updating the flexibility reserve schedule at each time step using a smart contract, by tabulating the flexibility metrics “variation in generation capacity (VIGC)” and uncertainty quantification of epistemic and aleatory uncertainties in variable distributed energy resources (DERs) generation and loads, is also presented.

The conventional power distribution network is being transformed drastically due to high penetration of renewable energy sources (RES) and energy storage. The optimal scheduling and dispatch is important to better harness the energy from intermittent RES. Traditional centralized optimization techniques limit the size of the problem and hence distributed techniques are adopted. The distributed optimization technique partitions the power distribution network into sub-networks which solves the local sub problem and exchanges information with the neighboring sub-networks for the global update. This paper presents an adaptive spectral graph partitioning algorithm based on vertex migration while maintaining computational load balanced for synchronization, active power balance and sub-network resiliency. The parameters that define the resiliency metrics of power distribution networks are discussed and leveraged for better operation of sub-networks in grid connected mode as well as islanded mode. The adaptive partition of the IEEE 123-bus network into resilient sub-networks is demonstrated in this paper.

The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, which limits the size of the problem that can be solved, and also creates a high communication overhead since all the data is transferred to the central controller. These limitations are addressed by a proposed consensus-ADMM (alternating direction method of multiplier) based distributed optimization algorithm, which decomposes the optimization problem into sub-problems. The distribution feeder is partitioned into low coupling sub-networks/regions, which solves the sub-problem locally and exchanges information with the neighboring regions to reach consensus to solve for the global update. The information exchange and synchronization between sub-networks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a virtual aggregator for synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for 0.5-MW laboratory microgrid for one hour ahead and day-ahead for IEEE 123-bus and EPRI J1 test feeder, and results are compared with a centralized solution.