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This paper presents a new decentralized optimization-based approach for an aggregator involved in residential demand response programs. The proposed approach relies on an existing two-phase optimization framework, whereby prosumers optimize first their self-generation and household consumption, which are subsequently coordinated by the aggregator via incentives through the solution of a mixed-integer linear program. As a distinctive aspect, the notion of fairness in the distribution of incentives among prosumers is incorporated through new nonlinear constraints based on the Shapley Value while respecting standard demand response requirements. A mathematical approximation, which is proven to be exact under certain practical assumptions, is proposed to accommodate the new constraints into the linear model. Furthermore, a novel solution methodology, which combines the divide-and-conquer principle and Dantzig–Wolfe decomposition, is devised to tackle the resulting problem in a distributed fashion. For validation purposes, the behavior of the linear approximations is examined by comparing the results with those attained with their nonlinear counterparts. Moreover, scenarios for prosumers featuring different flexibility levels are generated to assess the practicality of the assumptions adopted. Simulations also show the scalability of the proposed tool by considering cases with up to 10,000 prosumers with computing times within an hour if distributed computing is implemented. Furthermore, as compared to classical Shapley Value approaches, a similar distribution of incentives is attained by the new solution methodology with relative less and acceptable computational effort. Finally, the impact of the new constraints on Peak-to-Average Ratio is quantified, obtaining an increment of up to 13.2% with respect to cases disregarding fairness. • Fair allocation of incentives is integrated in residential demand response. • The notion of Shapley Value is used to quantify prosumers' contribution. • Practical assumptions are adopted to obtain linear expressions for incentives. • Prosumers privacy is preserved using a new optimization-based solution approach. • Experimental results with real-world data show the superiority of our approach. [ABSTRACT FROM AUTHOR]

Abstract: The reasons why the transmission network is a potentially attractive target for deliberate outages are twofold: (i) its crucial importance as a critical infrastructure for the society welfare, and (ii) its high level of vulnerability due to the current operation close to its static and dynamic limits. This new context where destructive agents come into play has been recognized by several agencies in Europe and North America, and various initiatives have been launched worldwide in order to assess and mitigate the vulnerability of transmission. Within this framework, this paper proposes the reinforcement and expansion of the transmission network as a way of mitigating the impact of increasingly plausible deliberate outages. The network planner selects the new lines to be built accounting not only for economic issues, as traditionally done, but also for the vulnerability of the transmission network against a set of credible intentional outages. The resulting vulnerability- and economic-constrained transmission expansion planning problem is formulated as a mixed-integer linear program. A number of case studies numerically illustrate the tradeoff between economic- and vulnerability-related issues and its impact on the expansion plans. In addition, we compare the results with those achieved by a traditional expansion planning model based on cost minimization. [Copyright &y& Elsevier]

This paper proposes a two-phase optimization framework for users that are involved in demand response programs. In a first phase, responsive users optimize their own household consumption, characterizing not only their appliances and equipment but also their comfort preferences. Subsequently, the aggregator exploits in a second phase this preliminary non-coordinated solution by implementing a coordination strategy for the aggregated loads while preserving users' privacy. The second phase relies on the solution of a bilevel program in which the aggregator's profit is maximized in the upper level while ensuring that the aggregated residential users do not incur any economic or comfort losses by participating in the demand response program. The lower level models the users' reaction to the aggregator's requests. As major complicating aspects, the resulting bilevel problem features nonlinear terms and lower-level binary variables. This challenging problem is addressed by a mixed-integer linear single-level reformulation and the application of an exact solution technique based on Dantzig–Wolfe decomposition. Simulations with up to 10,000 residential users illustrate the advantages of the proposed two-phase framework in terms of users' privacy, computational efficiency, and scalability. • Residential demand response is examined from an aggregator's perspective. • A two-phase framework to coordinate users' consumption is proposed. • Bilevel optimization reformulated as mixed-integer linear single-level. • An exact distributed approach is devised preserving users' privacy at no extra costs. • Real-life instances with up to 10,000 users are successfully solved. [ABSTRACT FROM AUTHOR]

Highlights: [•] We examine the consumer payment minimization problem by mixed-integer linear programming. [•] Quadratic and piecewise linear supply offers, and inter-temporal constraints are modeled. [•] As a salient feature, no heuristic manipulation or control parameter tuning are required. [•] Optimal or high-quality near-optimal solutions are attained in moderate computing times. [•] Inter-temporal constraints enlarge payment reductions with slight cost increases. [ABSTRACT FROM AUTHOR]