Hrvoje Pandžić

Capacity Optimization of Renewable Energy Sources and Battery Storage in an Autonomous Telecommunication Facility

This paper describes a robust optimization approach to minimize the total cost of supplying a remote telecommunication station exclusively by renewable energy sources (RES). Due to the intermittent nature of RES, such as photovoltaic (PV) panels and small wind turbines, they are normally supported by a central energy storage system (ESS), consisting of a battery and a fuel cell. The optimization is carried out as a robust mixed-integer linear program (RMILP), and results in different optimal solutions, depending on budgets of uncertainty, each of which yields different RES and storage capacities. These solutions are then tested against a set of possible outcomes, thus simulating the future operation of the system. Since battery cycling is inevitable in this application, an algorithm that counts the number of cycles and associated depths of discharges (DoD) is applied to the optimization results. The annual capacity reduction that results from these cycles is calculated for two types of battery technologies, i.e., valve-regulated lead-acid (VRLA) and lithium-ion (Li-ion), and treated as an additional cost. Finally, all associated costs are added up and the ideal configuration is proposed.

Control of Smart Grid Architecture

Abstract One of the main microgrid requirements is to operate in the most cost-effective way in a long run. Generally, microgrid, besides utilizing the local resources, can trade electricity in energy market, either directly or through an aggregator. Therefore, an optimization layer that optimizes microgrid resources with respect to their technical constraints, as well as external electricity prices, is essential for guiding the control layer toward the goal of operating at minimum cost. The optimization layer is usually a 24-h look-ahead optimization model, which is compatible with todays market structures and energy trading framework. This chapter formulates an optimization problem that minimizes the microgrid operating cost. However, since some of the parameters are uncertain, e.g., wind turbine output, photovoltaics output, and market prices, the initial deterministic formulation is expanded to capture this uncertainty. Thus, stochastic, robust, and interval optimization models are presented as well. An illustrative case study is derived in order to show the mechanics and specifics of all four formulations.

Coordination of Regulated and Merchant Storage Investments

Distributed transmission-scale energy storage is becoming economically feasible due to the growing share of renewable generation and cost reduction of specific storage technologies, primarily batteries. Under these circumstances, independent merchants may start investing in storage facilities. On the other hand, system operators, besides investing in transmission lines, may, under certain conditions, invest in storage units as well. This paper formulates a trilevel model where the upper-level problem optimizes the system operators transmission line and energy storage investments, the middle-level problem determines merchant energy storage investment decisions, while the lower level problem simulates market clearing process for representative days. After replacing the lower level problem with its primal dual equivalent conditions, the middle- and lower level problems are merged into a mixed-integer problem with equilibrium constraints. The resulting bilevel structure is iteratively solved using a cutting plane algorithm. The proposed formulation is first applied to a six-bus system to present the mechanics of the model and then to the IEEE RTS-96 test system. The results show that even at the low cost of energy storage, the system operator (SO) still prefers line investments, while merchant investments are driven by the volatility of LMPs. Both the SO and merchant investments increase the social welfare, although this increase is mostly driven by the SO investments.