A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids

Cooperating interconnected microgrids with the Distribution System Operation (DSO) can lead to an improvement in terms of operation and reliability. This paper investigates the optimal operation and scheduling of interconnected microgrids highly penetrated by renewable energy resources (DERs). Moreover, an efficient stochastic framework based on the Unscented Transform (UT) method is proposed to model uncertainties associated with the hourly market price, hourly load demand and DERs output power. Prior to the energy management, a newly developed linearization technique is employed to linearize nodal equations extracted from the AC power flow. The proposed stochastic problem is formulated as a single-objective optimization problem minimizing the interconnected AC MGs cost function. In order to validate the proposed technique, a modified IEEE 69 bus network is studied as the test case

Distribution market as a ramping aggregator for grid flexibility support

The growing proliferation of microgrids and distributed energy resources in distribution networks has resulted in the development of Distribution Market Operator (DMO). This new entity will facilitate the management of the distributed resources and their interactions with upstream network and the wholesale market. At the same time, DMOs can tap into the flexibility potential of these distributed resources to address many of the challenges that system operators are facing. This paper investigates this opportunity and develops a distribution market scheduling model based on upstream network ramping flexibility requirements. That is, the distribution network will play the role of a flexibility resource in the system, with a relatively large size and potential, to help bulk system operators to address emerging ramping concerns. Numerical simulations demonstrate the effectiveness of the proposed model on when tested on a distribution system with several microgrids.Comment: IEEE PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 16-19 Apr. 201

Managing risks of market price uncertainty for a microgrid operation

After deregulation of electricity in the United States, the day-ahead and real-time markets allow load serving entities and generation companies to bid and purchase/sell energy under the supervision of the independent system operator (ISO). The electricity market prices are inherently uncertain, and can be highly volatile. The main objective of this thesis is to hedge against the risk from the uncertainty of the market prices when purchasing/selling energy from/to the market. The energy manager can also schedule distributed generators (DGs) and storage of the microgrid to meet the demand, in addition to energy transactions from the market. The risk measure used in this work is the variance of the uncertain market purchase/sale cost/revenue, assuming the price following a Gaussian distribution. Using Markowitz optimization, the risk is minimized to find the optimal mix of purchase from the markets. The problem is formulated as a mixed integer quadratic program. The microgrid at Illinois Institute of Technology (IIT) in Chicago, IL was used as a case study. The result of this work reveals the tradeoff faced by the microgrid energy manager between minimizing the risk and minimizing the mean of the total operating cost (TOC) of the microgrid. With this information, the microgrid energy manager can make decisions in the day-ahead and real-time markets according to their risk aversion preference. The assumption of market prices following Gaussian distribution is also verified to be reasonable for the purpose of hedging against their risks. This is done by comparing the result of the proposed formulation with that obtained from the sample market prices randomly generated using the distribution of actual historic market price data --Abstract, page iii