Dépôt numérique
RECHERCHER

Decision making for smart grids with renewable energy.

Nguyen, Hieu Trung (2017). Decision making for smart grids with renewable energy. Thèse. Québec, Université du Québec, Institut national de la recherche scientifique, Doctorat en télécommunications, 188 p.

[img]
Prévisualisation
PDF
Télécharger (2MB) | Prévisualisation

Résumé

We are at the dawn of the smart grid era where there is significant increase in the demand-side participation in the grid’s operations. One important smart grid research topic concerns active demand-side management which can potentially result in great benefits for different involved grid entities (e.g., electricity customers, utility, resource aggregators) and enable to support increasing penetration of distributed renewable energy resources. However, efficient design for different decision making problems must be conducted to to realize the potential impacts of the active demand-side management, which is the focus of this dissertation. Specifically, we study three decision making problems of the corresponding demand-side smart grid entities considering integration of renewable energy sources, which are smart home energy management, smart load serving entity (LSE) pricing design, and cost allocation for cooperative demand-side resource aggregators (DRAs) under the virtual power plant (VPP) framework. Our research has resulted in three major contributions, which are presented in three main chapters of this dissertation. The first contribution is related to the efficient energy scheduling design for smart homes equipped with solar assisted thermal load. This design is conducted under the time-varying dynamic pricing scheme which can potentially bring great demand response benefits for home electric consumers. Specifically, we develop a rolling two-stage stochastic programming based algorithm, which aims to minimize the electricity cost, guarantee user comfort, and efficiently utilize renewable energy resources. We also propose to exploit the solar assisted thermal load for the energy management and analyze the impacts of different parameters on the smart home economic improvements. The second contribution concerns the development of a dynamic pricing scheme for a load serving entity (LSE) that can incentivize electric customers to provide demand response services. The design can effectively encourage participation of electric customers with flexibilities in energy consumption while not negatively affecting other electric customers lacking flexibilities in changing their energy consumption. Moreover, the proposed pricing scheme is compatible with the current market structure. Toward this end, we consider the pricing design as a bilevel optimization problem where the grid operator is the leader, who determines the demand response price, and the flexible customers are followers, whose energy consumption is adjusted in response to that price signal. We describe how to transform the proposed bilevel optimization problem, which is difficult to be solved directly, into an equivalent single objective mixed integer linear program (MILP), which can be solved efficiently by a branch and cut algorithm. Numerical results show that our proposed pricing design can be beneficial to both grid operator and electric customers. The third contribution aims to develop an efficient cost allocation scheme for cooperative demandside resource aggregators (DRA), which are coordinated under an emerging smart grid concept, namely, the virtual power plant (VPP). We address this problem by using the core based cooperative game theoretic approach. Since the core of the underlying game can contain many cost allocation solutions, our design enables us to choose an appropriate cost allocation solution inside the core that optimizes both stability and fairness metrics. This core based cost allocation problem is formulated as a largescale bi-objective optimization problem with an exponential number of implicit constraints related to the core definition. In particular, the parameters of these constraints are the function values of coalitions of DRAs, which are the outcomes of the optimal bidding strategies of the corresponding coalitions of DRAs. To tackle this highly complex bi-objective optimization problem, we propose to employ the -constraint and row constraint generation methods, which exploit the fact that the number of optimization variables can be much smaller than the number of optimization constraints. Numerical studies show that the proposed algorithm allows to construct the Pareto front with a large number of Pareto points for a VPP consisting of a large number of DRAs. Moreover, the proposed framework enables the VPP to determine a suitable cost allocation for its members considering the trade-off between stability and fairness.

Type de document: Thèse Thèse
Directeur de mémoire/thèse: Le, Long Bao
Mots-clés libres: smart grid; efficient energy scheduling design; smart homes; dynamic pricing scheme; load serving entity; cost allocation scheme; cooperative demand-side resource aggregators; virtual power plant
Centre: Centre Énergie Matériaux Télécommunications
Date de dépôt: 12 févr. 2018 21:27
Dernière modification: 12 févr. 2018 21:27
URI: http://espace.inrs.ca/id/eprint/6534

Actions (Identification requise)

Modifier la notice Modifier la notice