In the face of unprecedented challenges of upcoming fossil fuel shortage and reliability and security of the grid, there is an increasing interest in adopting distributed, renewable, energy resources, such as microgrids (MGs), and engaging flexible electric loads in power system operations to potentially drive a paradigm shift in energy production and consumption patterns. Prior work on MG dispatch has leveraged decentralized technologies like combined heat and power (CHP) and heat pumps to promote efficiency and economic gains; however, the flexibility of demand has yet to be fully exploited in cooperation with the grid to offer added benefits and ancillary services. The object of the study is to develop microgrid optimal dispatch with demand response (MOD-DR), which fills in the gap by coordinating both the demand and supply sides in a renewable-integrated, storage-augmented, DR-enabled MG to achieve economically viable and system-wide resilient solutions. The key contribution of this paper is the formulation of a multi-objective optimization with prevailing constraints and utility trade-off based on the model of a large-scale MG with flexible loads, which leads to the derivation of strategies that incorporate uncertainty in scheduling. Evaluation using real datasets is conducted to analyze the uncertainty effects and demand response potentials, demonstrating in a campus
In power systems, the main grid might be a group of several interconnected areas. The areas can be self-governing with their own polices and rules. According to the concept of system of systems (SoS) engineering, this paper presents a decentralized decision-making framework to determine an economical hourly generation schedule for a multi-area power system. Each self-governing area is modeled as an independent system, and the entire power system is modeled as a SoS. The proposed decentralized unit commitment algorithm takes into account the privacy of each independent system, and only a limited data information such as power exchange between the areas, needs to be exchanged between the systems. An iterative decentralized optimization model is presented to find the optimal operating point of all independent systems in the SoS-based power system architecture. The numerical results show the effectiveness of the proposed SoS framework and solution methodology.Index Terms-Multi-area power system, system of systems, decentralized decision-making framework, unit commitment.
This article demonstrates the benefits of simultaneous cooptimization on a 312-bus network representation of the Western Interconnection power grid with emphasis on The Bonneville Power Administration’s operational area in the Pacific Northwest. While generation and transmission expansion planning has traditionally been solved sequentially, simultaneous cooptimization of both guarantees plans at least as cost effective as sequential approaches and better integrates high-quality remote resources like wind into power grids. For three scenarios with varied carbon and transmission costs, results indicate that (1) simultaneous cooptimization provides up to 6 billion dollars in net present value benefits over sequential optimization during the 50-year planning horizon, (2) cooptimization is more adept at tapping into superior remote resources like wind that the sequential approach has trouble identifying for low iterations, and (3) 10 iterations of sequential cooptimization only capture 75%–96% of the transmission benefits of simultaneous cooptimization.
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