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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., Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada

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., Comment: 9 pages, 9 figures, in proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada

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.

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.

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.

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., Comment: 10 pages, 7 figures, submitted to Elsevier Energy

This paper proposes a novel cost-reflective and computationally efficient method for allocating distribution network costs to residential customers. First, the method estimates the growth in peak demand with a 50% probability of exceedance (50POE) and the associated network augmentation costs using a probabilistic long-run marginal cost computation based on the Turvey perturbation method. Second, it allocates these costs to customers on a cost-causal basis using the Shapley value solution concept. To overcome the intractability of the exact Shapley value computation for real-world applications, we implement a fast, scalable and efficient clustering technique based on customers' peak demand contribution, which drastically reduces the Shapley value computation time. Using customer load traces from an Australian smart grid trial (Solar Home Electricity Data), we demonstrate the efficacy of our method by comparing it with established energy- and peak demand-based cost allocation approaches.

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.

The home energy management problem has many different facets, including economic viability, data uncertainty and quality of strategy employed. The existing literature in this area focuses on individual aspects of this problem without a detailed, holistic analysis of the results with regards to practicality in implementation. In this paper, we fill this gap by performing a comprehensive comparison of seven different energy management strategies, each with different levels of practicality, sophistication and computational requirements. We analyse the results in the context of these three characteristics, and also critique the modelling assumptions made by each strategy. Our analysis finds that using a more sophisticated energy management strategy may not necessarily improve the performance and economic viability of the PV-battery system due to the effects of modeling assumptions, such as the treatment of uncertainties in the input data and battery degradation effects.

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.

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., Comment: 6 pages, 4 figures, conference paper

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., Comment: 10 pages, 7 figures

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., Comment: 6 pages, 7 figures, Published in IEEE Conference: 2017 Australasian Universities Power Engineering Conference (AUPEC)

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., Comment: 10 pages, 9 figures, submitted to IEEE Transactions on Smart Grids

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., Comment: To be published. 7 pages, 20th Power Systems Computation Conference (PSCC 2018), Jun 2018

The uptake of behind-the-meter distributed energy resources in low-voltage distribution networks has reached a level where network issues have started to emerge, which requires new tools for operation and planning. In this paper, we propose a methodology for synthesizing stochastic demand and generation profiles for unobserved customers with rooftop PV, called prosumers. The proposed model bridges the gap between the limited available empirical data, and the large amount of high-quality, stochastic demand and generation data required for probabilistic analysis. The approach employs clustering analysis and a Dirichlet-categorical hierarchical model of the features of unobserved prosumers. Based on the data of clusters of prosumers, Markov chain models of demand and generation profiles are constructed from empirical data, and synthetic demand profiles are subsequently sampled from these. The sampled traces are cross-validated and show a good statistical fit to the observed data. Two case studies are considered to confirm the validity of the proposed methodology. The first studies the impact of behavioral differences on the synthetic demand profiles, while the second looks at the impact of varying solar generation penetration on demand profiles.

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., Comment: This paper is uploaded here just for reference. It is not published yet

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).

The paper proposes a computationally efficient electricity market simulation tool (MST) suitable for future grid scenario analysis. The market model is based on a unit commitment (UC) problem and takes into account the uptake of emerging technologies, like demand response, battery storage, concentrated solar thermal generation, and HVDC transmission lines. To allow for a subsequent stability assessment, the MST requires an explicit representation of the number of online generation units, which affects powers system inertia and reactive power support capability. These requirements render a fullfledged UC model computationally intractable, so we propose unit clustering, rolling horizon approach, and constraint reduction to increase the computational efficiency. To showcase the capability of the proposed tool, we use a simplified model of the Australian National Electricity Market with different penetrations of renewable generation. The results show that the number of online units resulting from the proposed tool is very close to the binary UC run over a week-long horizon, which is confirmed by the loadability and inertia analysis. That confirms the validity of the approach for long term future grid studies, where one is more interested in finding weak points in the system rather than in a detailed analysis of individual operating conditions.

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., Comment: 10 pages, 7 figures, 2 tables. Submitted for publicatiob to IEEE Transactions on Power Systems

The increasing integration of wind generation is accompanied with a growing concern about secure and reliable power system operation. Due to the intermittent nature of wind, the base-load units need to cycle significantly more than they were designed for, resulting in reduced life cycle and increased costs. Therefore, it is becoming necessary for wind turbines to take part in frequency control, and reduce the need for additional ancillary services provided by conventional generators. In this paper, we propose an optimised operation strategy for the wind farms. In this strategy, we maximise the kinetic energy of wind turbines by an optimal combination of the rotor speed and the pitch angle. We exploit the wake interaction in a wind farm, and de-load some of the up-wind turbines. We show that the kinetic energy accumulated in the rotating masses of the WTs can be increased compared to the base case without compromising efficiency of the wind farm. In a specific system, we show that by implementing this strategy, and injecting the stored kinetic energy of the WTs' rotors into the system during a frequency dip, we can delay the system frequency nadir up to 30 s., Comment: Presented in the 13th Wind Integration Workshop Berlin, Nov 11 - 13 2014

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., Comment: 11 pages, 6 figures, 2 tables, Accepted for publication in IEEE Transactions on Smart Grid

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., Comment: 7 pages. To appear in Proc. 19th Power Syst. Comput. Conf. (PSCC), Genoa, Italy, 2016

A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an aggregator that aims to minimize the cost of electricity purchased in a pooled wholesale market. This paper presents a fast distributed algorithm for aggregating a large number of households with a mixture of discrete and continuous energy levels. A distinctive feature of the method in this paper is that the nonconvex DR problem is decomposed in terms of households as opposed to devices, which allows incorporating more intricate couplings between energy storage devices, appliances and distributed energy resources. The proposed method is a fast distributed algorithm applied to the double smoothed dual function of the adopted DR model. The method is tested on systems with up to 2560 households, each with 10 devices on average. The proposed algorithm is designed to terminate in 60 iterations irrespective of system size, which can be ideal for an on-line version of this problem. Moreover, numerical results show that with minimal parameter tuning, the algorithm exhibits a very similar convergence behavior throughout the studied systems and converges to near-optimal solutions, which corroborates its scalability., Comment: Accepted in IEEE Transactions on Smart Grid

This letter presents a fast distributed algorithm for aggregating a large number of households with mixed-integer variables and intricate couplings between devices. The proposed fast distributed gradient algorithm is applied to the double smoothed dual function of the adopted DR model. The results also show that, with minimal parameter adjustments, the convergence of the dual objective exhibits the same behavior irrespective of the system size., Comment: 2 pages, 1 figure, to be published in IEEE Transactions on Smart Grid Letters

This paper considers a demand-side aggregation scheme specifically for large numbers of small loads, such as households and small and medium-sized businesses. We introduce a novel auction format, called a staggered clock-proxy auction (SCPA), for on-line scheduling of these loads. This is a two phase format, consisting of: a sequence of overlapping iterative ascending-price clock auctions, one for each time-slot over a finite decision horizon, and; a set of proxy auctions that begin at the termination of each individual clock auction, and which determine the final price and allocation for each time-slot. The overlapping design of the clock phases grant bidders the ability to effectively bid on inter-temporal bundles of electricity use, thereby focusing on the most relevant parts of the price-quantity space. Since electricity is a divisible good, the proxy auction uses demand-schedule bids, which the aggregator uses to compute a uniform-price partial competitive equilibrium for each time slot. We show that, under mild assumptions on the bidders' utilities functions, the proxy phase implements the Vickrey-Clarke-Groves outcome, which makes straightforward bidding in the proxy phase a Bayes-Nash equilibrium. Furthermore, we demonstrate the SCPA in a scenario comprised of household agents with three different utility function types, and show how the mechanism enables efficient on-line energy use scheduling., Comment: 11 pages, 7 figures, submitted to IEEE Transactions on Smart Grids

With the increased penetration of intermittent renewable energy sources (RESs) in future grids (FGs), balancing between supply and demand will become more dependent on demand response (DR) and energy storage. Thus, FG feasibility studies will need to consider DR for modelling nett future demand. Against this backdrop, this paper proposes a demand model which integrates the aggregated effect of DR in a simplified representation of the effect of market/dispatch processes aiming at minimising the overall cost of supplying electrical energy. The conventional demand model in the optimisation formulation is augmented by including the aggregated effect of numerous users equipped with rooftop photovoltaic (PV)-storage systems. The proposed model is suited for system studies at higher voltage levels in which users are assumed to be price anticipators. As a case study, the effect of the demand model is studied on the load profile, balancing and loadability of the Australian National Electricity Market in 2020 with the increased penetration of RESs. The results are compared with the demand model in which users are assumed to be price takers., Comment: 8 pages, 6 Figures, submitted to the Sustainable Energy, Grids and Networks. arXiv admin note: text overlap with arXiv:1412.3143

It is anticipated that penetration of renewable energy sources (RESs) in power systems will increase further in the next decades mainly due to environmental issues. In the long term of several decades, which we refer to in terms of the future grid (FG), balancing between supply and demand will become dependent on demand actions including demand response (DR) and energy storage. So far, FG feasibility studies have not considered these new demand-side developments for modelling future demand. In Australia, installed rooftop photovoltaic (PV) generation has been increasing significantly in recent years, and this is increasingly influence the nett load profile for FG feasibility studies. This paper proposes an aggregate nett load or demand model to be used at higher voltage levels considering the effect of rooftop PV, energy storage and DR for FG scenarios, which is inspired by the smart home concept. The proposed model is formulated as an optimization problem aiming at minimizing the electricity cost. As a case study, the effect of the load model is studied on the load profile, balancing and loadability of the Australian National Electricity Market in 2020 with the increased penetration of wind and solar generation., Comment: 11 pages, 6 figures, 3 Tables, submitted to IET Generation, Transmission & Distribution