Gregor Verbič

Sleiman Mhanna, Gregor Verbic, Archie Chapman

DC power flow approximations are ubiquitous in the electricity industry. However, these linear approximations fail to capture important physical aspects of power flow, such as the reactive power and voltage magnitude, which are crucial in many applications to ensure voltage stability and AC solution feasibility. This paper proposes two LP approximations of the AC optimal power flow problem, founded on tight polyhedral approximations of the SOC constraints, in the aim of retaining the good lower bounds of the SOCP relaxation and relishing the computational efficiency of LP solvers. The high accuracy of the two LP approximations is corroborated by rigorous computational evaluations on systems with up to 9241 buses and different operating conditions. The computational efficiency of the two proposed LP models is shown to be comparable to, if not better than, that of the SOCP models in most instances. This performance is ideal for MILP extensions of these LP models since MILP is computationally more efficient than MIQCP.

Yu Yi, Gregor Verbic

The increasing penetration of distributed energy resources in low-voltage networks is turning end-users from consumers to prosumers. However, the incomplete smart meter rollout and paucity of smart meter data due to the regulatory separation between retail and network service provision make active distribution network management difficult. Furthermore, distribution network operators oftentimes do not have access to real-time smart meter data, which creates an additional challenge. For the lack of better solutions, they use blanket rooftop solar export limits, leading to suboptimal outcomes. To address this, we designed a conditional generative adversarial network (CGAN)-based model to forecast household solar generation and electricity demand, which serves as an input to chance-constrained optimal power flow used to compute fair operating envelopes under uncertainty.

Donald Azuatalam, Archie C. Chapman, Gregor Verbič

Given the historically static nature of low-voltage networks, distribution network companies do not possess tools for dealing with an increasingly variable demand due to the high penetration of distributed energy resources (DER). Within this context, this paper proposes a probabilistic framework for tariff design that minimises the impact of DER on network performance, stabilise network company revenue, and improves the equity of network costs allocation. To address the issue of the lack of customers' response, we also show how DER-specific tariffs can be complemented with an automated home energy management system (HEMS) that reduces peak demand while retaining the desired comfort level. The proposed framework comprises a nonparametric Bayesian model which statistically generates synthetic load and PV traces, a hot-water-use statistical model, a novel HEMS to schedule customers' controllable devices, and a probabilistic power-flow model. Test cases using both energy- and demand-based network tariffs show that flat tariffs with a peak demand component reduce the customers' cost, and alleviate network constraints. This demonstrates, first, the efficacy of the proposed tool for the development of tariffs that are beneficial for networks with a high DER penetration, and second, how customers' HEM systems can be part of the solution.

Zahra Rahimpour, Gregor Verbic, Archie C. Chapman

This paper focuses on energy management in buildings with phase change material (PCM), which is primarily used to improve thermal performance, but can also serve as an energy storage system. In this setting, optimal scheduling of an HVAC system is challenging because of the nonlinear and non-convex characteristics of the PCM, which makes solving the corresponding optimization problem using conventional optimization techniques impractical. Instead, we use dynamic programming (DP) to deal with the nonlinear nature of the PCM. To overcome DP's curse of dimensionality, this paper proposes a novel methodology to reduce the computational burden, while maintaining the quality of the solution. Specifically, the method incorporates approaches from sequential decision making in artificial intelligence, including macro actions and multi-time scale Markov decision processes, coupled with an underlying state-space approximation to reduce the state-space and action-space size. The performance of the method is demonstrated on an energy management problem for a typical residential building located in Sydney, Australia. The results demonstrate that the proposed method performs well with a computational speed-up of up to 12,900 times compared to the direct application of DP.

Yiju Ma, Donald Azuatalam, Thomas Power et al.

Battery storage, particularly residential battery storage coupled with rooftop PV, is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management(HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.

Yiju Ma, Mohammad Seydali Seyf Abad, Donald Azuatalam et al.

Energy storage systems (EES) are expected to be an indispensable resource for mitigating the effects on networks of high penetrations of distributed generation in the near future. This paper analyzes the benefits of EES in unbalanced low voltage (LV) networks regarding three aspects, namely, power losses, the hosting capacity and network unbalance. For doing so, a mixed integer quadratic programmming model (MIQP) is developed to minimize annual energy losses and determine the sizing and placement of ESS, while satisfying voltage constraints. A real unbalanced LV UK grid is adopted to examine the effects of ESS under two scenarios: the installation of one community ESS (CESS) and multiple distributed ESSs (DESSs). The results illustrate that both scenarios present high performance in accomplishing the above tasks, while DESSs, with the same aggregated size, are slightly better. This margin is expected to be amplified as the aggregated size of DESSs increases.

Jaysson Guerrero, Archie Chapman, Gregor Verbic

The increasing uptake of distributed energy resources (DERs) in distribution systems and the rapid advance of technology have established new scenarios in the operation of low-voltage networks. In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions. Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario. A major challenge to implementing P2P energy trading is that of ensuring that network constraints are not violated during the energy exchange. This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints. The proposed method is tested on a typical UK low-voltage network. The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture.

Ahmad Shabir Ahmadyar, Shariq Riaz, Gregor Verbic et al.

The increasing penetration of non-synchronous renewable energy sources (NS-RES) alters the dynamic characteristic, and consequently, the frequency behaviour of a power system. To accurately identify these changing trends and address them in a systematic way, it is necessary to assess a large number of scenarios. Given this, we propose a frequency stability assessment framework based on a time-series approach that facilitates the analysis of a large number of future power system scenarios. We use this framework to assess the frequency stability of the Australian future power system by considering a large number of future scenarios and sensitivity of different parameters. By doing this, we identify a maximum non-synchronous instantaneous penetration range from the frequency stability point of view. Further, to reduce the detrimental impacts of high NS-RES penetration on system frequency stability, a dynamic inertia constraint is derived and incorporated in the market dispatch model. The results show that such a constraint guarantees frequency stability of the system for all credible contingencies. Also, we assess and quantify the contribution of synchronous condensers, synthetic inertia of wind farms and a governor-like response from de-loaded wind farms on system frequency stability. The results show that the last option is the most effective one.

Ruidong Liu, Gregor Verbic, Jin Ma

Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. To achieve the computational speed-up, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.