arXiv subjects

Gregor Verbic

Publications and source records attributed to Gregor Verbic.

At least 19 recordsLinked to original sources

arXiv2026

Dynamic Network Prices for Prosumer-aware Hosting Capacity Management

The fast uptake of distributed energy resources (DERs) presents increasing challenges for managing hosting capacity in distribution networks. Existing solutions include direct load control, operating envelopes, and price-based control through dynamic energy prices. Despite their effectiveness, these methods often rely on assumed prosumer behavioural patterns and overlook prosumers' desire to retain control over their devices. Additionally, current fixed or Time-of-Use (ToU) prices are based on spatial and temporal averages, having limited impact on network conditions and DER operation. To address these limitations, this paper proposes a bilevel optimisation framework that explicitly models prosumer decision-making in the design of dynamic network prices. The upper level represents the distribution system operator (DSO), setting network prices under cost-recovery and network constraints, while the lower level models prosumers optimising DER operation in response. The proposed framework preserves customer prerogative, enhances DER flexibility, and offers actionable insights for network hosting capacity management and the evolution of network tariff structures under high DER penetration.

eess.SY↗

arXiv2026

Dynamic Passivity Multipliers for Plug-and-Play Stability Certificates of Converter-Dominated Grids

Ensuring small-signal stability in power systems with a high share of inverter-based resources (IBRs) is hampered by two factors: (i) device and network parameters are often uncertain or completely unknown, and (ii) brute-force enumeration of all topologies is computationally intractable. These challenges motivate plug-and-play (PnP) certificates that verify stability locally yet hold globally. Passivity is an attractive property because it guarantees stability under feedback and network interconnections; however, strict passivity rarely holds for practical controllers such as Grid Forming Inverters (GFMs) employing P-Q droop. This paper extends the passivity condition by constructing a dynamic, frequency-dependent multiplier that enables PnP stability certification of each component based solely on its admittance, without requiring any modification to the controller design. The multiplier is parameterised as a linear filter whose coefficients are tuned under a passivity goal. Numerical results for practical droop gains confirm the PnP rules, substantially enlarging the certified stability region while preserving the decentralised, model-agnostic nature of passivity-based PnP tests.

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arXiv2024

A Proportional-Integral Model for Fractional Voltage Tripping of Distributed Energy Resources

In regions with high shares of distributed energy resources (DERs), massive disconnection of small-scale DERs in low-voltage distribution grids during disturbances poses a serious threat to power system security. However, modeling this effect in a computationally efficient way remains challenging. This paper proposes a novel proportional-integral aggregate model for predicting the fraction of tripped DERs based on the voltage at the substation connection point. The model effectively captures the cumulative behavior of the system, is simple to implement, and includes seven parameters for undervoltage tripping and seven for overvoltage tripping behavior, each with a distinct physical meaning. We further propose an optimization-based approach to tune the model parameters. Simulation results show significantly more accurate predictions compared to the DER\_A model -- a standard dynamic model for aggregate DER behavior -- even when the latter is optimized, with only a minor increase in model complexity.

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arXiv2022

Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts

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.

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arXiv2022

Power Hardware-In-the-Loop Testing of a Peer-to-Peer Energy Trading Framework

This paper demonstrates the value of power hardware-in-the-loop (PHIL) testing for the study of peer-to-peer (P2P) energy trading. P2P has emerged as a promising candidate for coordinating large numbers of distributed energy resources (DER) that pose a risk to network operations if left unmanaged. The existing literature has so far relied on pure software simulations to study DER and distribution networks within this context. This requires the development of simplified models for complex components due to the computational limitations involved. Issues that arise through the operation of physical hardware in real-world applications are therefore neglected. We present PHIL testing as a solution to this problem by exhibiting its ability to capture the complex behaviors of physical DER devices. A high-fidelity PHIL test environment is introduced that combines key hardware elements with a simulated network model to study a P2P trading scenario. The initial findings reveal several underlying challenges of coordinating DER that are not typically discussed in prior works.

eess.SY↗

arXiv2022

Fair Coordination of Distributed Energy Resources with Volt-Var Control and PV Curtailment

This paper presents a novel distributed optimal power flow (DOPF) method for fair distributed energy resource (DER) coordination in the context of mandated rooftop PV inverter control modes. In practice, inverter reactive power control is increasingly required by grid connection codes, which often unfairly curtail PV generation of prosumers towards the end of low-voltage feeders. Similarly, optimization-based DER coordination methods that aim solely for technically-efficient DER coordination do not consider the distribution of PV curtailment across customers. To address these concerns, we develop a tractable multi-objective DOPF method for optimal DER coordination that (i) curtails PV generation fairly across prosumers, and (ii) incorporates a standard piecewise-linear volt-var control reactive power control function without using integer variables. Three equity principles representing different interpretations of fairness are implemented in our coordination method; namely, egalitarian, proportional and uniform dynamic PV curtailment redistribution. The performance of our approach is demonstrated on low-voltage distribution feeders of different sizes (5, 10, 25, 50 and 100 prosumers) using two network topologies: line topology without lateral spurs and tree topology with lateral spurs. Each network considers three levels of PV penetration, giving 30 test systems in total. The results demonstrate the effectiveness of the proposed DOPF method for fair DER coordination: PV curtailment is equitably distributed among prosumers with a computational burden on par with conventional DOPF approaches. Moreover, different fairness methods result in different patterns of curtailment, which a regulator may choose between.

cs.CE↗

arXiv2022

Practical Considerations of DER Coordination with Distributed Optimal Power Flow

The coordination of prosumer-owned, behind-the-meter distributed energy resources (DER) can be achieved using a multiperiod, distributed optimal power flow (DOPF), which satisfies network constraints and preserves the privacy of prosumers. To solve the problem in a distributed fashion, it is decomposed and solved using the alternating direction method of multipliers (ADMM), which may require many iterations between prosumers and the central entity (i.e., an aggregator). Furthermore, the computational burden is shared among the agents with different processing capacities. Therefore, computational constraints and communication requirements may make the DOPF infeasible or impractical. In this paper, part of the DOPF (some of the prosumer subproblems) is executed on a Raspberry Pi-based hardware prototype, which emulates a low processing power, edge computing device. Four important aspects are analyzed using test cases of different complexities. The first is the computation cost of executing the subproblems in the edge computing device. The second is the algorithm operation on congested electrical networks, which impacts the convergence speed of DOPF solutions. Third, the precision of the computed solution, including the trade-off between solution quality and the number of iterations, is examined. Fourth, the communication requirements for implementation across different communication networks are investigated. The above metrics are analyzed in four scenarios involving 26-bus and 51-bus networks.

cs.CE↗

arXiv2021

Can phase change materials in building insulation improve self-consumption of residential rooftop solar? An Australian case study

This work investigates the extent to which phase change material (PCM) in the building's envelope can be used as an alternative to battery storage systems to increase self-consumption of rooftop solar photovoltaic (PV) generation. In particular, we explore the electricity cost-savings and increase in PV self-consumption that can be achieved by using PCMs and the operation of the heating, ventilation, and air conditioning (HVAC) system optimised by a home energy management system (HEMS). In more detail, we consider a HEMS with an HVAC system, rooftop PV, and a PCM layer integrated into the building envelope. The objective of the HEMS optimisation is to minimise electricity costs while maximising PV self-consumption and maintaining the indoor building temperature in a preferred comfort range. Solving this problem is challenging due to PCM's nonlinear characteristics, and using methods that can deal with the resulting non-convexity of the optimisation problem, like dynamic programming is computationally expensive. Therefore, we use multi-timescale approximate dynamic programming (MADP) that we developed in our earlier work to explore a number of Australian PCM scenarios. Specifically, we analyse a large number of residential buildings across five Australian capital cities. We find that using PCM can reduce annual electricity costs by between 10.6% in Brisbane and 19% in Adelaide. However, somewhat surprisingly, using PCM reduces PV self-consumption by between 1.5% in Brisbane and 2.7% in Perth.

eess.SY↗

arXiv2019

Macro-action Multi-time scale Dynamic Programming for Energy Management in Buildings with Phase Change Materials

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.

cs.LG↗

arXiv2019

Multi-Stage Compound Real Options Valuation in Residential PV-Battery Investment

Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that undervalue the investment. In our work, a real options valuation (ROV) framework is proposed to determine the optimal strategy for executing multiple interacting options within a distribution network investment, to mitigate the risk of financial losses in the presence of future uncertainties. To demonstrate the characteristics of the proposed framework, we determine the optimal strategy to economically justify the investment in residential PV-battery systems for additional grid supply during peak demand periods. The options to defer, and then expand, are considered as multi-stage compound options, since the option to expand is a subsequent option of the former. These options are valued via the least squares Monte Carlo method, incorporating uncertainty over growing power demand, varying diesel fuel price, and the declining cost of PV-battery technology as random variables. Finally, a sensitivity analysis is performed to demonstrate how the proposed framework responds to uncertain events. The proposed framework shows that executing the interacting options at the optimal timing increases the investment value.

econ.EM↗

arXiv2018

A Novel Probabilistic Framework to Study the Impact of PV-battery Systems on Low-Voltage Distribution Networks

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.

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arXiv2018

Impacts of Community and Distributed Energy Storage Systems on Unbalanced Low Voltage Networks

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.

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arXiv2018

Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network

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.

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arXiv2018

A Study of Energy Trading in a Low-Voltage Network: Centralised and Distributed Approaches

Over the past years, distributed energy resources (DER) have been the object of many studies, which recognise and establish their emerging role in the future of power systems. However, the implementation of many scenarios and mechanism are still challenging. This paper provides an overview of a local energy market and explores the approaches in which consumers and prosumers take part in this market. Therefore, the purpose of this paper is to review the benefits of local markets for users. This study assesses the performance of distributed and centralised trading mechanisms, comparing scenarios where the objective of the exchange may be based on individual or social welfare. Simulation results show the advantages of local markets and demonstrate the importance of advancing the understanding of local markets.

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arXiv2018

Accelerated Methods for the SOCP-relaxed Component-based Distributed Optimal Power Flow

In light of the increased focus on distributed methods, this paper proposes two accelerated subgradient methods and an adaptive penalty parameter scheme to speed-up the convergence of ADMM on the component-based dual decomposition of the second-order cone programming (SOCP) relaxation of the OPF. This work is the first to apply an adaptive penalty parameter method along with an accelerated subgradient method together in one scheme for distributed OPF. This accelerated scheme is demonstrated to reach substantial speed-ups, as high as 87%, on real-world test systems with more than 9000 buses, as well as on other difficult test cases.

cs.CE↗

arXiv2017

Generic Demand Model Considering the Impact of Prosumers for Future Grid Scenario Analysis

The increasing uptake of residential PV-battery systems is bound to significantly change demand patterns of future power systems and, consequently, their dynamic performance. In this paper, we propose a generic demand model that captures the aggregated effect of a large population of price-responsive users equipped with small-scale PV-battery systems, called prosumers, for market simulation in future grid scenario analysis. The model is formulated as a bi-level program in which the upper-level unit commitment problem minimizes the total generation cost, and the lower-level problem maximizes prosumers' aggregate self-consumption. Unlike in the existing bi-level optimization frameworks that focus on the interaction between the wholesale market and an aggregator, the coupling is through the prosumers' demand, not through the electricity price. That renders the proposed model market structure agnostic, making it suitable for future grid studies where the market structure is potentially unknown. As a case study, we perform steady-state voltage stability analysis of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show that a high prosumer penetration changes the demand profile in ways that significantly improve the system loadability, which confirms the suitability of the proposed model for future grid studies.

math.OC↗

arXiv2017

A Component-Based Dual Decomposition Method for the OPF Problem

This paper proposes a component-based dual decomposition of the nonconvex AC optimal power flow (OPF) problem, where the modified dual function is solved in a distributed fashion. The main contribution of this work is that is demonstrates that a distributed method with carefully tuned parameters can converge to globally optimal solutions despite the inherent nonconvexity of the problem and the absence of theoretical guarantees of convergence. This paper is the first to conduct extensive numerical analysis resulting in the identification and tabulation of the algorithmic parameter settings that are crucial for the convergence of the method on 72 AC OPF test instances. Moreover, this work provides a deeper insight into the geometry of the modified Lagrange dual function of the OPF problem and highlights the conditions that make this function differentiable. This numerical demonstration of convergence coupled with the scalability and the privacy preserving nature of the proposed method makes it well suited for smart grid applications such as multi-period OPF with demand response (DR) and security constrained unit commitment (SCUC) with contingency constraints and multiple transmission system operators (TSOs).

cs.DC↗

arXiv2017

A Framework for Frequency Stability Assessment of Future Power Systems: An Australian Case Study

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.

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