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In this paper, the potential of reducing the electricity cost with the help of energy storage and controllable loads on the Russian retail electricity market is studied. The retail electricity market in Russia features different price categories, allowing consumers to reduce expenses with proper planning. Approximately 2/3 of the cost in flexible price categories is for the power consumption in specific hours, with the remaining 1/3 for the energy consumption itself. Because of the continued cost decrease of energy storage devices, they have become an increasingly more attractive investment, opening opportunities for economic savings combined with load shifting, which can occur either in a standalone manner or with the use of energy storage. In this paper, demand shifting is implemented in the Russian retail market, combined with the payback calculation for energy storage.

Modern power systems problems recognize the value of using stochastic information to model renewable generation operations properly. Many optimal operation and planning problems can be formulated as two-stage stochastic optimization problems, typically employing discrete probabilistic scenarios to describe stochastic information. Large discrete scenario sets increase the computational complexity of these problems. Therefore, it is essential scenario reduction techniques that provide a good representative scenario set yielding the same or similar optimal results as the original probabilistic set.This paper describes a generalized adaptive partition-based method (GAPM) for finding an optimal representative scenario set for two-stage stochastic optimization problems. The iterative methodology obtains the optimal partition for a scenario set while providing the optimality gap at each iteration. Numerical tests are performed to compare the GAPM with popular scenario reduction techniques employed in the power systems literature. We show that even for a simple small problem, popular scenario reduction techniques based on the Kantorovich distance exhibit optimally gaps of more than 5%, performing even worse than vanilla methods of scenario reduction. Our methodology, in principle, can guarantee any optimality gap between the original problem and the reduced one.

Substation reconfiguration may reduce congestion and therefore reduce the cost of economic dispatch. In this paper we propose a novel method that can be applied to any substation configuration using a mixed-integer linear model. Our methodology generalizes substation reconfiguration to include both more traditional transmission switching and bus splitting. The problem is NP-hard but we show that using state-of-the-art tools we can reduce the computation time significantly. The method has been evaluated on several test cases, the IEEE 118-bus case being the largest. Optimal solutions were found within much smaller time frames than have been previously reported.

Dynamic line rating technology has been evolving since the late nineteen seventies. However, it is not used to its full extend. This study presents a methodology for calculating the benefits of dynamic line rating in the Russian power grid. The economic benefits of using a dynamic line rating instead of a conventional static line rating are estimated for a simple but representative two-zone network representing a multi-unit Russian energy corridor connecting the Siberian and European zones. Due to the lack of precise information on the generation units’ costs and capacity, we present an inverse optimization model to infer these parameters from the available electricity price and aggregated consumption. Our simulations show that using dynamic line rating versus static rating results in savings of up to 1.65% per day.

Given the rise of electric vehicle (EV) adoption, supported by government policies and dropping technology prices, new challenges arise in the modeling and operation of electric transportation. In this paper, we present a model for solving the EV routing problem while accounting for real-life stochastic demand behavior. We present a mathematical formulation that minimizes travel time and energy costs of an EV fleet. The EV is represented by a battery energy consumption model. To adapt our formulation to real-life scenarios, customer pick-ups and drop-offs were modeled as stochastic parameters. A chance-constrained optimization model is proposed for addressing pick-ups and drop-offs uncertainties. Computational validation of the model is provided based on representative transportation scenarios. Results obtained showed a quick convergence of our model with verifiable solutions. Finally, the impact of electric vehicles charging is validated in Downtown Manhattan, New York by assessing the effect on the distribution grid.

This paper presents an analysis of the stability and quality of the distributed generation planning problem’s investment solution. A two-stage stochastic programming model is used to find the optimal distributed generators’ installed capacities. We emphasize the design of scenarios to represent the stochasticity of power production from renewable sources. For scenario generation, a method is proposed based on the clustering of real measurements of meteorological variables. The quality and stability of the optimal investment solutions are thoroughly analysed as a function of the number of selected scenarios. The results show that a reduced selection of scenarios can give an inadequate solution to distributed generators’ investment strategy.

Demand flexibility is the capacity of demand-side loads to change their consumption at any instant. It makes electricity more affordable by helping customers to use less power when prices are high. On the other hand, demand flexibility can also help to increase the reliability of the power grid when is highly stressed, by reducing demand for power or for integrating renewable generation. Thermostatic controlled loads (TCLs) are one of the most promising options among demand response (DR) solutions however, conventional methods for controlling single TCLs are not easily extensible to aggregated TCLs since it may cause them to synchronize. In addition, the ambient temperature may significantly influence the power flexibility offered by the TCLs. In this paper, a metric of flexibility is applied along with a modified control algorithm to de-synchronize the TCLs with the aim of fairly comparing the different control approaches applied to aggregated TCLs. Furthermore, a sensitivity analysis, considering variations of temperature in several periods of time, is performed over the TCLs power flexibility. The results were validated in Simulink/MATLAB using real demand and generation data from UK national system and simultaneously, temperature data for the same region and time frame.

The integration of energy systems such as electricity and gas grids and power and thermal grids can bring significant benefits in terms of system security, reliability, and reduced emissions. Another alternative coupling of sectors with large potential benefits is the power and transportation networks. This is primarily due to the increasing use of electric vehicles (EV) and their demand on the power grid. Besides, the production and operating costs of EVs and battery technologies are steadily decreasing, while tax credits for EV purchase and usage are being offered to users in developed countries. The power grid is also undergoing major upgrades and changes with the aim of ensuring environmentally sustainable grids. These factors influence our work. We present a new operating model for an integrated EV-grid system that incorporates a set of aggregators (owning a fleet of EVs) with partial access to the distribution grid. Then, the Cooperative Game Theory is used to model the behavior of the system. The Core is used to describe the stability of the interaction between these aggregators, and the Shapley value is used to assign costs to them. The results obtained show the benefit of cooperation, which could lead to an overall reduction in energy consumption, reduced operating costs for electric vehicles and the distribution grid, and, in some cases, the additional monetary budget available to reinforce the transmission and grid infrastructures.