Proactive Resilient Scheduling for Networked Microgrids With Extreme Events

Abstract

In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. ''Budget of uncertainty'' and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.

The Spectrum of Proactive, Resilient Multi-Microgrid Scheduling: A Systematic Literature Review

“…Selected key contributions. [48] 2012-09-28 Integration of dynamic topology option into economic microgrid operation [49] 2014-04-09 Optimal scheduling of DER considering the risk of outages [27] 2014-09-26 Interaction of microgrids with and without independent islanding capabilities [50] 2016-06-13 Value of reconfigurable microgrids in integrating Electric Vehicles (EVs) [20] 2016-08-10 Resilient scheduling of microgrids affected by uncertainty [51] 2016-08-26 Multi-objective scheduling of microgrids considering normal operation costs and the risk of load curtailment [52] 2016-10-15 Microgrid scheduling considering operating costs, emissions, and reserve requirements [53] 2017-08-17 Robust formulation of the optimal proactive scheduling problem for microgrids [28] 2017-12-13 Risk-based strategies for multi-microgrid scheduling considering stochastic RESs [54] 2018-02-16 Optimal scheduling for hybrid AC/DC multi-microgrids [55] 2018-02-20 Integration of Demand Response (DR) programs and grid reconfiguration into microgrid asset scheduling [56] 2018-03-26 Resilient asset scheduling in reconfigurable microgrids [57] 2018-05-11 Security-constrained dispatch for microgrids using multi-objective optimization [58] 2018-08-17 Resiliency enhancements by optional scheduling of networked microgrids [59] 2018-12-28 Impact of scheduling discrepancies on interconnected microgrids [60] 2019-04-18 Microgrid scheduling combining flexible time frame DER scheduling and single time interval-based optimal dispatch [61] 2019-07-02 Resilient scheduling of networked multi-microgrids using a three-stage approach [62] 2019-08-07 Proactive, resilient scheduling of interconnected microgrids [63] 2019-08-16 Distributed energy management of interconnected microgrids considering adversarial actions [64] 2020-01-06 Optimal, resilient operation of dynamic multi-microgrids…”

“…Evidently, the authors did not present the first multi-microgrid setup, ever [7,28], but a significant contribution was made in considering resilience aspects in multi-microgrid scheduling. Since then, several authors also directly considered multi-microgrid setups in resilient scheduling formulations [54,58,[61][62][63][64]. Although only in very recent years identified key contributions show a tendency to explicitly cover multi-microgrids, literature that focuses on independent microgrids may be applicable in the extended setting as well.…”

“…In case only a generic model is considered, no specific model is listed although the specific asset may be covered by the generic model as well. Some publications such as [64] explicitly use the term battery energy storage but no significant difference to the formulation of generic EESs including [28,62] was found. Hence, these battery energy storage models were classified as generic EESs as well.…”

“…Most of the key contributions stick to generic DER and EES models without specifically focusing on more specific assets. Only one paper also considers Combined Heat and Power (CHP) plants and the thermal needs of a microgrid [62]. Similarly, one out of 20 papers specifically targets Micro Turbines (MTs) [28] and two contributions take EVs into account [20,50].…”

“…For unscheduled DERs, most key contributions use a stochastic or robust formulation without purely relying on a forecast and optimizing security margins. Although for generic unscheduled DERs several authors including [27,53,54,62] chose to follow an indeterministic formulation, most of the contributions that tackle the specifics of uncontrollable or partly controllable generation such as Photovoltaics (PV) and wind power plants, deploy stochastic asset models. Most of the specific asset models describe the conversion of the primary energy source, into electric energy [28,58].…”