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Demand response (DR) has been increasingly growing in significance among the solutions to tackle climate change, by supporting the development of intermittent renewable energy sources in the smart grid. Many models based on mathematical optimization have been developed to address the challenge of supporting residential customers in providing flexibility services to the grid. However, comparing and applying those models is not always straightforward because of particular data handling or specific assumptions. In this work, we take advantage of the common aspects of DR models to build a metamodel, and hence an open source Python library that aims to unify the concepts and the data streaming in and out of the underlying mathematical optimization models. We demonstrate the effectiveness of the metamodel and of the Python library by using it to implement a task scheduler and to optimize the energy consumption for two dwellings. [ABSTRACT FROM AUTHOR]

The Time-and-Level-of-Use (TLOU) system is a recently developed approach for electric energy pricing, extending Time-of-Use with an energy capacity that customers can book in advance for a given consumption time. We define a bilevel optimization model for determining the pricing parameters of TLOU, maximizing the supplier revenue while anticipating an optimal reaction of the customer. A solution approach is built, based on the discrete finite set of optimality candidates of the lower-level customer problem. [ABSTRACT FROM AUTHOR]

The optimal reactive power dispatch (ORPD) problem is an alternating current optimal power flow (ACOPF) problem where discrete control devices for regulating the reactive power, such as shunt elements and tap changers, are considered. The ORPD problem is modeled as a mixed-integer nonlinear optimization problem and its complexity is increased compared to the ACOPF problem, which is highly nonconvex and generally hard to solve. Recently, convex relaxations of the ACOPF problem have attracted a significant interest since they can lead to global optimality. We propose a tight conic relaxation of the ORPD problem and show that a round-off technique applied with this relaxation leads to near-global optimal solutions with very small guaranteed optimality gaps, unlike with the nonconvex continuous relaxation. We report computational results on selected MATPOWER test cases with up to 3375 buses. [ABSTRACT FROM AUTHOR]

Demand-response aggregators are faced with the challenge of how to best manage numerous and heterogeneous distributed energy resources (DERs). This paper proposes a decentralized methodology for optimal coordination of DERs. The proposed approach is based on Dantzig–Wolfe decomposition and column generation, thus allowing to integrate any type of resource whose operation can be formulated within a mixed-integer linear program. We show that the proposed framework offers the same guarantees of optimality as a centralized formulation, with the added benefits of distributed computation, enhanced privacy, and higher robustness to changes in the problem data. The practical efficiency of the algorithm is demonstrated through extensive computational experiments, on a set of instances generated using data from Ontario energy markets. The proposed approach was able to solve all test instances to proven optimality, while achieving significant speed-ups over a centralized formulation solved by state-of-the-art optimization software. [ABSTRACT FROM AUTHOR]

The classical alternating current optimal power flow problem is highly nonconvex and generally hard to solve. Convex relaxations, in particular semidefinite, second-order cone, convex quadratic, and linear relaxations, have recently attracted significant interest. The semidefinite relaxation is the strongest among them and is exact for many cases. However, the computational efficiency for solving large-scale semidefinite optimization is lower than for second-order cone optimization. We propose a conic relaxation obtained by combining semidefinite optimization with the reformulation-linearization technique, commonly known as RLT. The proposed relaxation is stronger than the second-order cone relaxation and nearly as tight as the standard semidefinite relaxation. Computational experiments using standard test cases with up to 6515 buses show that the time to solve the new conic relaxation is up to one order of magnitude lower than for the chordal relaxation, a semidefinite relaxation technique that exploits the sparsity of power networks. [ABSTRACT FROM AUTHOR]

This paper examines the polytope of feasible power generation schedules in the unit commitment (UC) problem. We provide computational results comparing formulations for the UC problem commonly found in the literature. We introduce a new class of inequalities, giving a tighter description of feasible operating schedules for generators. Computational results show that these inequalities can significantly reduce overall solution times. [ABSTRACT FROM PUBLISHER]

Smart homes have the potential to achieve efficient energy consumption: households can profit from appropriately scheduled consumption. By 2020, 35% of all households in North America and 20% in Europe are expected to become smart homes. Developing a smart home requires considerable investment, and the householders expect a positive return. In this context, this work addresses the following question: what and/or when equipment should be bought for a specific site to gain a positive return on the investment? This work proposes a framework to guide the smart-home transition considering customized electricity usage. The framework is based on linear models and gives a simple payback analysis of each combination of equipment acquisition for any specific user taking into account geographical location and local conditions. It also possible to use the framework for equipment sizing. The results quantify the dependence of the simple payback on the site and the application. • Modeling and optimization of the integration of smart home components. • General framework for the optimal set of smart appliances and investment timing. • Maximization of the return on investment. • Economic feasibility shown for three case studies in different countries using real data. • Quantification of the payback for each site and application. [ABSTRACT FROM AUTHOR]

• Hydro-Québec can provide annual storage ranging from 3 to 6 TW h for high VRE markets. • Price arbitraging with high VRE markets leads to sustainable changes for hydropower. • Hydropower maintenance flexibility will gain more value with VRE growth. • Revenues from price arbitraging with high VRE markets remain low. Northeastern North America is transitioning rapidly from fossil fuel generation to variable renewable energy (VRE) sources such as wind and solar. Their integration into electricity grids poses challenges in terms of matching supply with demand. Once VRE represent a large share of installed capacity, intermittency and variability of their production will become a major issue. Hydro-Québec, the public electric utility in the Canadian province of Québec, has a large hydropower generation fleet. Its storage capacity enables adjusting hydro production, when the system needs it, to support VRE integration. In this paper, potential impacts of VRE growth within neighbouring grids are simulated through operational production management models. The results show that Hydro-Québec has the potential to facilitate this energy transition but that the current electricity market structure does not provide incentives for an optimal contribution. [ABSTRACT FROM AUTHOR]