Showing papers by "Alessandra Parisio published in 2022"

Optimization-based Ramping Reserve Allocation of BESS for AGC Enhancement

Abstract: This paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for addressing an ongoing challenge in Automatic Generation Control (AGC) enhancement, i.e., the optimal coordination of multiple Battery Energy Storage Systems (BESSs). While exploiting further the synergy between BESSs and slow ramping resources, the proposed scheme offers an insight into the energy-neutral operation, which is achieved by smoothly discontinuing the BESS participation along with the minimization of Area Injection Error (AIE), a variant of traditional Area Control Error (ACE). The first stage of ORRA is to incorporate Neural Networks (NNs) with the AIE in order to ensure a zero-mean of ramping reserves to be allocated among BESSs. These AIE signals are then used to formulate the optimal coordination of BESS as an online optimization problem, which is therefore feedback-driven. Finally, a distributed optimization algorithm is developed to solve the formulated problem in real-time, achieving a sublinear dynamic regret that quantifies the cost difference to the trajectory computed by a centralized optimizer with perfect global information. Consistent with the geographical distribution of BESSs, the proposed ORRA is fully distributed such that the algorithm can be executed in parallel at all nodes. Simulations on a modified IEEE 14-bus system are performed to illustrate the effectiveness and important features of ORRA.

Optimization-Based Ramping Reserve Allocation of Aggregated BESS for AGC Enhancement

Abstract: —This paper considers Automatic Generation Control (AGC) enhancement by exploring the synergy between conventional generators and an aggregated Battery Energy Storage System (BESS). In practice, it has been observed that BESSs sometimes inefficiently contribute to the minimization of Area Control Error (ACE). This phenomenon is open to several interpretations. On the one hand, cost-effective aggregation of BESSs has not been well-established. On the other hand, BESSs could be driven into an inefficient operation due to miscalculated ACE. Moreover, some BESSs may have to operate in the opposite direction than desired to maintain energy neutrality. Therefore, this paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for BESS aggregation, addressing the three issues simultaneously. The underlying methodology is to formulate the BESS aggregation problem as an distributed online optimization problem and solve it in real-time, where corrected ACE is leveraged to quantify the instantaneous ramping requirements. Another distinctive feature of ORRA lies in its ability to immediately deploy and smoothly un-deploy the aggregated BESS with respect to a frequency event, thereby contributing to energy-neutral operation. The optimization algorithm is fully distributed and can guarantee fair and near-optimal allocation in real-time. Simulations on a modified IEEE 14-bus system are performed to illustrate the effectiveness of ORRA.

On Controlling Battery Degradation in Vehicle-to-Grid Energy Markets

Abstract: Nowadays, power grids are facing reduced total system inertia as traditional generators are phased out in favor of renewable energy sources. This issue is expected to deepen with the increasing penetration of electric vehicles (EVs). The influence of a single EV on power networks is low; nevertheless, the aggregate impact becomes relevant when they are properly coordinated. In this context, we consider the frequent case of a group of EVs connected to a parking lot with a photovoltaic facility. We propose a novel strategy to optimally control their batteries during the parking session, which is able to satisfy their requirements and energy constraints. EVs participate in a noncooperative energy market based on a smart pricing mechanism that is designed in order to increase the predictability and flexibility of the aggregate parking load. Differently from the existing contributions, we employ a novel approach to minimize the degradation of batteries. The effectiveness of the proposed method is validated through numerical experiments based on a real scenario.

Optimal Coordination of Electric Vehicles for Voltage Support in Distribution Networks

Abstract: The increase in electric vehicles (EVs) and distributed generations (DGs) has brought significant voltage fluctuation issues to distribution networks. Since simultaneous active and reactive power coordination can make voltage regulation more efficient, optimal coordination of active/reactive EV charging/discharging is an attractive solution. This work contributes to addressing the challenge above by devising an optimisation-based coordination framework for EV charging/discharging for voltage support in distribution networks. The proposed framework is based on model predictive control (MPC), and includes reactive/active power management, as well as EV behaviour modelling. The proposed framework minimises network voltage deviation and electricity cost of electric vehicle charging stations (EVCSs). An EV behaviour modelling is integrated into the MPC scheme by using a Markov Chain Monte Carlo (MCMC) method. A case study is conducted on a 33-bus distribution network with photovoltaic (PV) generation and multiple EVCSs, demonstrating effective voltage regulation and cost-saving of the proposed MPC-based optimisation framework.

Optimization-Based Ramping Reserve Allocation in AGC Avoiding Counterproductive Regulation

Abstract: It has been observed in practice that Battery Energy Storage Systems (BESSs) may not always contribute to the minimization of Area Control Error (ACE). This phenomenon of “counterproductive regulation” is open to a number of interpretations. For one thing, cost-effective coordination of distributed BESSs has not been well-established and requires much more research efforts. For another, the BESSs could be driven into an inefficient operation due to miscalculated ACE. Moreover, some BESSs may have to operate in the opposite direction than desired in order to recover an energy-neutral state. Leveraging corrected ACE signals, this paper explores a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) to coordinate a group of BESSs in Automatic Generation Control (AGC). The underlying methodology is to counteract net-load forecasting errors by providing only ramping reserves rather than capacity reserves to AGC, based on which an online optimization problem is formulated to minimize the associated battery usage cost of all nodes. The corresponding optimization algorithm is purely distributed and can guarantee fair and near-optimal allocation in real-time while avoiding those counterproductive behaviors mentioned above. Simulations on a modified IEEE 14bus system are performed to illustrate the effectiveness of ORRA in both allocation and AGC enhancement.

Reliability Optimization of Multi-Energy System Considering Energy Storage Devices Effects under Weather Uncertainties

Abstract: Multi-energy systems (MES) allow various energy forms, such as electricity, gas, and heat, to interact and achieve energy transfer and mutually benefit, reducing the probability of load cutting in the event of a failure, increasing the energy utilization efficiency, and improving the reliability and robustness of the overall energy supply system. Since energy storage systems can help to restore power in the case of failure and store the surplus energy to enhance the flexibility of MES, this work provides a methodology for reliability optimization, considering different energy storage configuration schemes under weather uncertainties. First of all, a reliability evaluation model of a multi-energy system under weather uncertainties based on a sequential Monte Carlo simulation is established. Then, the reliability optimization problem is formulated as a multi-objective optimization problem to minimize the reliability index, SAIDI (system average interruption duration index), and the reliability cost. Finally, a case study implemented on a typical MES layout is used to demonstrate the proposed methodology. A comparative analysis of three widely adopted multi-objective metaheuristic algorithms, including NSGA-II (non-dominated sorting genetic algorithm II), MOPSO (multiple objective particle swarm optimization), and SPEA2 (strength Pareto evolution algorithm 2), is performed to validate the effectiveness of the proposed method. The simulation results show that the NSGA-II algorithm leads to better optimal values and converges the fastest compared to the other two methods.

Soft open points based load restoration for the urban integrated energy system under extreme weather events

Abstract: With frequent occurrences of extreme natural disasters such as typhoons in urban integrated energy systems (UIES), it is of great significance to cope with different kinds of disturbances. This paper proposes a load restoration method under typhoon weather for urban distribution and natural gas networks based on soft open points (SOP). Firstly, the typhoon wind speed model is introduced and the line fault rates of distribution networks under typhoons are calculated. Secondly, the gas turbine and electric-driven compressor are considered as the coupling units of the integrated electric–gas energy system, and related models are constructed. The fault analysis method of the natural gas network is proposed considering the faults in the distribution network lead by typhoons. Thirdly, SOP installed in the distribution network with the V/f control mode is applied to restore electrical loads and provide voltage support for the loads on the fault side. After that, the loads of the gas network could also be restored because of the restoration of the coupling units. Optimal energy flow is applied to determine the output of the power and gas sources, coupling units and also the loads to be restored. Finally, the fault rate of each line under typhoon disaster is analysed and the correctness and effectiveness of the resilience improving method based on SOP are verified with test systems UIES E33-G14 and UIES E123-G48.