• Energy Research

Harmonic distortions have adverse effects on the operation of the power system. Harmonic sources in low voltage distribution grids (LVDG) are highly dispersed in nature.Therefore, managing the resulting voltage and current distortions through conventional measures, such as passive or active power filters, becomes challenging. This work coordinates the harmonic injection capabilities of enhanced photovoltaic inverters to form a distributed active power filter. The proposed method aims to reduce the voltage and current distortions of the LVDG with fair utilization of inverters. Towards this direction, a lexicographic optimization scheme is formulated to derive the inverters’ harmonic current set-points by (i) minimizing the harmonic voltage limit violations, (ii) maximizing the inverter utilization fairness based on Jain’s fairness index, and (iii) minimizing the compensating currents of the inverters to reduce their lifetime degradation. The resulting optimization model is non-convex, thus a convex second-order cone program (SOCP) is formulated by relaxing the non-convex constraints and reformulating the Jain’s index as an SOCP constraint. Moreover, an iterative algorithm is developed to find the feasible operating point that maximizes the Jain’s fairness value. Simulation results indicate the superiority of the proposed scheme compared to existing methodologies in mitigating harmonic distortions in three-phase four-wire LVDGs.

The performance of long-term power system studies are vital to evaluate the system reliability and efficiency under an increasing penetration of renewable energy sources (RES). This work develops a long-term unit commitment (UC) model considering combined-cycle (CC) units to enable the performance of annual studies in power systems. Specifically, a simplified configuration-based model of the CC units is used that decreases the number of configurations to reduce the computational complexity. The considered UC problem is formulated as a mixed-integer linear program (MILP), which is intractable when a long-term horizon is used. To make the problem tractable, a rolling horizon approach is proposed to solve the long-term MILP problem, generating high-quality approximate solutions. The proposed approach is applied to a real isolated power system and an annual study is carried out to examine the impact of an increasing RES penetration on the system operation.

The evolution of power distribution grids from passive to active systems creates reliability and efficiency challenges to the distribution system operators. In this paper, an energy management and control scheme for managing the operation of an active distribution grid with prosumers is proposed. A multi-objective optimization model to minimize (i) the prosumers electricity cost and (ii) the cost of the grid energy losses, while guaranteeing safe and reliable grid operation is formulated. This is done by determining the active and reactive power set-points of the photovoltaic and storage systems integrated in the grid buildings. The resulting optimization model is non-convex, thus a convex second-order cone program is developed by appropriately relaxing the non-convex constraints which yields optimal results in most operating conditions. The convexified model is further utilized to develop an algorithm that yields feasible solutions to the non-convex problem under any operating conditions. Moreover, a second novel algorithm to find the operating point that provides fairness between the prosumers and the grid costs is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed scheme in managing an industrial distribution grid compared to a self-consumption approach.

Energy storage systems (ESSs) are increasingly used in power system optimization by deriving different ESS mathematical models. The most widely-used model is the piecewise linear ESS model which utilizes non-convex constraints to represent the ESS power losses, resulting in challenging optimization problems. To reduce the problem complexity, convex relaxation models are often derived but may compromise the solution quality of the underlying problems. This work investigates the exact and relaxed versions of three different mathematical representations of the piecewise linear ESS model, in terms of their solution quality and execution efficiency. Towards this direction, the three ESS models are incorporated into the unit commitment problem which often violates the ESS relaxation exactness under ramping constraints. Simulation results present (a) the execution times of the exact and relaxed ESS models and (b) the optimality gap of the relaxed models when the relaxation exactness is violated.

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150%–170% and 160%–180% for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh.

Energy storage systems (ESSs) are increasingly used in power system optimization. Different ESS mathematical models are developed that consider nonlinear functions for power losses. However, these models require non-convex constraints to represent the ESS losses, resulting in challenging optimization problems. To reduce the complexity, convex relaxation models are often derived but generate infeasible solutions when the relaxation exactness is violated. To deal with this issue, this work develops two successive convexification algorithms that generate fast and high-quality feasible solutions when the derived solution is not exact. The first algorithm handles general loss functions, while the second algorithm enhances performance when piecewise-linear loss functions are used. Specifically, the algorithms reduce the feasible region of the relaxed ESS models using a tightening box trust region around the current solution in successive iterations. The proposed algorithms are applied to the Unit Commitment and Peak Shaving and Energy Arbitrage problems to investigate their performance considering piecewise-linear and quadratic ESS loss functions. Simulation results demonstrate the impact of the ESSs relaxation violation on the actual system operation and validate the algorithms efficacy to generate high-quality feasible and even optimal solutions with significantly lower execution times compared to problems utilizing exact ESS models.

Peak shaving applications provided by energy storage systems enhance the utilization of existing grid infrastructure to accommodate the increased penetration of renewable energy sources. This work investigates the provision of peak shaving services from a flywheel energy storage system installed in a transformer substation. A lexicographic optimization scheme is formulated to define the flywheel power set-points by minimizing the transformer power limit violations and the flywheel energy losses. Convex functions that represent the flywheel power losses and its maximum power are derived and integrated in the proposed scheme. A two-level hierarchical control framework is introduced to operate the transformer-flywheel-system in a way that handles prediction errors and modelling inaccuracies. At the higher level, a model predictive controller is developed that solves the lexicographic optimization scheme using linear programming. At the lower-level, a secondary controller corrects the power set-points of the model predictive controller using real-time measurements. A software platform has been developed for integrating the proposed controllers in an experimental setup to test their effectiveness in a realistic testbed setting, and the flywheel system characteristics are experimentally identified. Simulation and experimental results validate and verify the modeling, identification, control and operation of a real flywheel system for peak shaving services.