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This paper presents a novel price setting optimization problem for an electricity supplier in the smart grid. In this framework the supplier provides electricity to a residential load aggregator using Time-and-Level-of-Use prices (TLOU). TLOU is an energy pricing structure recently introduced in the literature, where the prices vary depending on the time and the level of consumption. This problem is formulated as a bilevel optimization problem, in which the supplier sets the prices that maximize the profit in a demand response context, anticipating the reaction of a residential load aggregator that minimizes total cost. These decisions are made in a competitive environment, while explicitly considering the aggregator's load shifting preferences and the level of consumption, and ensuring a user-friendly price structure. The optimization problem is reformulated as a single-level problem to be solved using off-the-shelf solvers. We present computational experiments to validate the performance of TLOU, and provide insights on the relationship between the user's demand flexibility, the capacity profile and the resulting structure of prices. We show that the supplier's economical benefit is increased up to 10% through the implementation of this type of demand response program, while providing savings of up to 6% for the consumers. • We present a framework to determine TLOU energy prices for an electricity retailer. • We formulate the problem as a bilevel optimization problem to set optimal prices. • We explicitly consider user preferences in the in the price setting problem. • Our model can be reformulated and solve by of-the-shelf solvers. • We provide evidence of the effect of TLOU on retailer's and end-users' performance. [ABSTRACT FROM AUTHOR]

• We define a new flexibility product for residential heat loads on energy markets. • We consider aggregating electric water heaters as an example of such a product. • We use a mean field game approach to quantify the flexibility with recovery limits. • Our approach allows explicit constraints on the post control recovery dynamics. • We assess the economic viability of our approach with Net Present Value estimates. Electric thermal loads, such as those for space heaters, water heaters and air conditioners, due to their association with energy storage, are deferrable. Thus, they can become an effective tool to compensate for the mismatches between power generation and power demand induced by renewable sources. Load reduction and load increase may appear to be equivalent to generation increase and generation withdrawal, but there is a significant difference in the associated post-load-control energy rebound phenomena. These phenomena can provoke undesirable post-load-control changes in the demand dynamics. The contribution of this paper is twofold. First, we define a flexibility product that load aggregators could propose that would involve a mean energy increase or decrease offer for a price and also guarantee bounds on the post-load-control deviations from normal. Second, we provide aggregators with the mathematical tools to specify the maximum load relief or increase possible under specific constraints on post-control recovery dynamics. The analysis rests on successive solutions of a linear program and exploits recent results on load control based on mean field game theory. We consider the case of aggregate electric water heater loads. [ABSTRACT FROM AUTHOR]

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]

We present a new model for finding the optimal placement of electric vehicle charging stations across a multiperiod time frame so as to maximise electric vehicle adoption. Via the use of stochastic discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. We adopt a simulation approach and precompute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Our major contribution is a reformulation into a maximum covering model, which uses the precomputed error terms to calculate the users covered by each charging station. This allows solutions to be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and greedy randomised adaptive search procedure heuristics to obtain good-quality solutions more efficiently. Extensive computational results are provided, and they compare the maximum covering formulation with the current state of the art for both exact solutions and the heuristic methods. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Hydro-Québec and the Natural Sciences and Engineering Research Council of Canada [Discovery Grant 2017-06054; Collaborative Research and Development Grant CRDPJ 536757–19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0185. [ABSTRACT FROM AUTHOR]

The evolving landscape of electrical grids has presented formidable challenges to energy systems, which need adaptable solutions for reliability, supply adequacy, and demand balance, where demand flexibility arises as a pivotal operational asset for these needs. In recent decades, desalination technology, particularly reverse osmosis, has been employed to purify seawater and brackish water by removing salt and impurities, effectively addressing water scarcity in some regions of the world. However, its energy-intensive nature, driven by high-pressure pump usage, leads to significant operational expenses and places additional strain on the power grid. Our study introduces a novel holistic model for the Chilean electricity system, incorporating reverse osmosis desalination facilities to comprehensively confront water scarcity through strategies that leverage the flexibility of high-pressure pumps. The model considers dispatch coordination, unveiling the synergy's value given mainly by the desalination system and the energy generation infrastructure. The study underscores the influence of desalination on power system dynamics, transmission, and generation expansion throughout the time horizon of 2025 to 2040. The principal findings underscore the advantages derived from co-optimizing desalination and the electrical system, the enhanced system performance from flexible desalination plant operations, and the synergistic potential of solar, battery energy storage, and the desalinated water system. These insights help to inform investment decisions, decarbonization policies, and the water–energy nexus interplay, charting a course towards comprehensive sustainability. • Co-optimization of electricity and desalinated water systems reach a more efficient power system transition. • Flexibility of desalination plants operation can be used as a tool to obtain more efficient power systems. • Synergies are observed when solar photovoltaic and battery storage are joined with desalination plants. • Levelised cost of water is smaller when flexible operation of RO desalination plants is used. • Investing in water storage allows to shift water production to lower electricity cost hours. [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]

• A novel optimization model to maximize energy for short-term hydropower unit commitment is proposed. • A MILP formulation based on the efficiency curves is established. • The optimized solutions can be directly implemented in practice. • A real case study in Canada shows benefits of optimal solutions compared to historical decisions. This paper presents a linear mixed-integer formulation to solve the short-term hydropower unit commitment problem. It uses the pair of maximum efficiency points of water discharge and the power produced at the maximum storage for all possible combinations of turbines. The goal is to maximize total energy production for all periods. The objective function is calculated using the correction between the power produced at the current volume and the maximum storage and penalizes unit start-ups. Constraints on the maximum number of turbine changes are imposed to find a viable solution. Computational results are reported for 65 instances with two powerhouses of five turbines each located in the Saguenay-Lac-St-Jean region of the province of Quebec in Canada. [ABSTRACT FROM AUTHOR]

With the increasing presence of renewable energy sources in the electrical power grid, demand response via thermostatic appliances such as electric water heaters is a promising way to compensate for the significant variability in renewable power generation. We propose a multistage stochastic optimization model that computes the optimal day-ahead target profile of the mean thermal energy contained in a large population of heaters, given various possible wind power production and uncontrollable load scenarios. This optimal profile is calculated to make the variable net demand as even as possible., (© The Author(s) 2020.)