Stavros Karagiannopoulos

Optimal planning of distribution grids considering active power curtailment and reactive power control

In this paper, a new planning methodology is proposed for existing distribution grids, considering both passive and active network measures. The method is designed to be tractable for large grids of any type, e.g., meshed or radial. It can be used as a decision-making tool by distribution system operators which need to decide whether to invest in new hardware, such as new lines and transformers, or to initiate control measures influencing the operational costs. In this paper, active power curtailment and reactive power control are taken into account as measures to prevent unacceptable voltage rises as well as element overloads, as these allow postponing network investments. A low-voltage, meshed grid with 27 nodes is used to demonstrate the proposed scheme. In this particular case, the results show that by using control measures, an active distribution system operator can defer investments and operate the existing infrastructure more efficiently. The methodology is able to account for variations in operational and investment costs coming from regulatory influences to provide an insight to the most cost-efficient decision.

Hybrid approach for planning and operating active distribution grids

This paper investigates the planning and operational processes of modern distribution networks hosting distributed energy resources (DERs). While in the past the two aspects have been distinct, a methodology is proposed in this paper to co-optimise the two phases by considering the operational flexibility offered by DERs already in the planning phase. By employing AC optimal power flow (OPF) to analyse the worst-case scenarios for the load and distributed generator injection, the optimal set-points for the DERs are determined such that the networks security is ensured. From these results, the optimised individual characteristic curves are then extracted for each DER which are used in the operational phase for the local control of the devices. The optimised controls use only local measurements to address system-wide issues and emulate the OPF solution without any communication. Finally, the proposed methodology is tested on the Cigre LV benchmark grid confirming that it is successful in mitigating with acceptable violations over- and under-voltage problems, as well as congestion issues. Its performance is compared against the OPF-based approach and currently employed local control schemes.

Battery energy storage capacity fading and control strategies for deterministic and stochastic power profiles

As manufacturing costs keep decreasing, battery energy storage systems (BESS) are expected to play a key role in modern grids. However, due to their energy constraints and internal losses, the restoration of the state of charge (SoC) to a reference range is of vital importance to fulfil their tasks. In this paper, we propose SoC control schemes based on existing ones, and then we evaluate their behavior in predictable and stochastic power system applications. The modifications include parameter tuning based on the actual BESS state, as well as alternating the control scheme according to forecasts of the application signal. Furthermore, we extend a Lithium-Ion battery model in order to quantify capacity degradation and hence, investigate the impact of the various SoC restoration strategies. Results show that potentials to increase the lifetime are application-dependent, based on the degree of flexibility allowed by a service. Overall, the calendar aging dominates the cycling aging and thus, there is limited space for improvement with different SoC control schemes. On the other hand, by incorporating forecast information, we can reduce the amount of energy needed for the SoC restoration and hence, decrease additional energy costs.

Scheduling and real-time control of flexible loads and storage in electricity markets under uncertainty

In many countries, groups of producers and consumers are organized into virtual entities to participate in electricity markets. These entities are called balance groups (BGs), or are given similar names, because they are responsible for maintaining an energy balance for the group, and experience costs in case of imbalances. With large shares of uncertain renewable energy sources (RES), BGs are exposed to the risk of high balancing costs. In this paper, we propose a day-ahead (DA) scheduling and a real-time (RT) control scheme to minimize the spot market and balancing costs of a BG using flexible loads and storage resources. In the DA scheduling problem, we account for RES and price uncertainties by formulating a two-stage stochastic optimization problem with recourse. The RT control problem is formulated as a stochastic model predictive control (MPC) problem that uses short-term RES forecasts. We demonstrate the performance of the proposed scheme considering a BG with a wind farm, an industrial load, and a pumped-storage plant. The results show that the proposed scheme reduces the BG costs, but the cost savings vary and are case dependent.

Co-optimisation of planning and operation for active distribution grids

Given the increased penetration of smart grid technologies, distribution system operators are obliged to consider in their planning stage both the increased uncertainty introduced by non-dispatchable distributed energy resources, as well as the operational flexibility provided by new real-time control schemes. First, in this paper, a planning procedure is proposed which considers both traditional expansion measures, e.g. upgrade of transformers, cables, etc., as well as real-time schemes, such as active and reactive power control of distributed generators, use of battery energy storage systems and flexible loads. At the core of the proposed decision making process lies a tractable iterative AC optimal power flow method. Second, to avoid the need for a real-time centralised coordination scheme (and the associated communication requirements), a local control scheme for the operation of individual distributed energy resources and flexible loads is extracted from offline optimal power flow computations. The performance of the two methods is demonstrated on a radial, low-voltage grid, and compared to a standard local control scheme.