Discrete optimization for supply demand matching in smart grids

2018-07-26Traditional power grids consisted of large grid operator owned generators to supply the demand of the consumers. The inertia of the large generators was able to handle any sudden demand fluctuations followed by an eventual ramp up/down of generation. However, modern smart grids, characterized by the proliferation of small sized Distributed Energy Resources (DER) such as photovoltaics, energy storage etc. lack this ability of handling demand fluctuations due to their lack of inertia. Therefore, they require proactive supply demand matching by utilizing the available advanced sensing and remote control capability. ❧ A plethora of work has been done assuming that the nodes (supply and demand) in the grid exhibit continuous operational values (generation/consumption values). Fine grained control is required for continuous operational values which might not be economically available. Thus, supply demand matching often needs to be performed over discrete sets of operational values of the nodes making this an NP-hard problem. While heuristics (with unbounded error) and Integer Program based algorithms (computationally expensive) have been studied extensively, the application of approximation algorithms to develop fast algorithms with bounded error guarantees has been limited. ❧ In this work, we address these limitations by developing a suite of polynomial runtime complexity approximation algorithms to perform supply demand matching in smart grids. We first model the smart grid to capture the available discrete control strategies for each node in the smart grid and the associated costs and uncertainties. We also model practical grid constraints such as the overhead associated with switching strategies and the smart grid network capacity constraints. We then develop approximation algorithms with a variety of objectives and constraints such as minimizing curtailment error in Demand Response, minimizing cost of supply demand matching, fairness etc. and theoretically provide the optimality guarantees. In addition to the theoretical analysis, we perform practical evaluations of the algorithms and show that our supply demand matching algorithms provide solutions which are close to optimal in a small amount of time