This paper introduces a demand-side management strategy aimed at minimising power imports from the grid supply point while preserving load composition in scenarios with low-carbon technologies and renewable energy integration. A detailed load modelling framework is developed to account for constant impedance, current, and power loads, as well as induction motors, electric vehicles, and heat pumps, capturing their temporal variability to enhance simulation accuracy in distribution networ ks with high penetration of low-carbon technologies. The proposed strategy incorporates advanced load payback criteria to maintain load composition before and after management actions, ensuring consistent steady-state and dynamic system behaviour. The study examines the impact of induction motors, electric vehicles, and heat pumps on load controllability, alongside the variability introduced by solar photovoltaics and wind generation. The results demonstrate that demand-side management effectively flattens load profiles, mitigates renewable-induced fluctuations, and enhances load flexibility by dynamically adjusting controllable loads. This approach ensures stable grid operation without compromising post-management load dynamics. The findings emphasise the crucial role of demand-side management in integrating low-carbon technologies, optimising energy efficiency, maximising renewable energy utilisation, and reducing grid stress, ultimately contributing to the sustainable operation of modern power systems.

The integration of Low Carbon Technologies (LCTs) and renewable energy sources has introduced significant technical challenges in distribution networks (DNs), including voltage rise, reverse power flows, and increased losses. Demand-side management (DSM) offers a viable solution to address these issues by optimizing load profiles, reducing peak loads, and mitigating the impacts of renewable energy intermittency. This paper proposes a novel DSM framework designed to enhance distribution network performance while maintaining steady-state and dynamic stability. The framework solves a multi-objective optimization problem using optimal power flow over a 24-hour horizon, incorporating constraints such as load payback, supply-demand balance, voltage limits, and line capacities. A composite load model, combining static and dynamic components, captures the voltage-dependent behaviour of loads. Additionally, probabilistic models for renewable energy generation are integrated to represent wind and solar variability. The study addresses the limitations of existing DSM strategies by incorporating internal DN constraints and monitoring time-varying performance indicators such as voltage stability and network losses. It further explores the coordinated optimization of DSM with renewable generation, leveraging advanced algorithms for load forecasting and scheduling. Case studies on a benchmark network demonstrate significant improvements in grid stability, operational efficiency, and energy flexibility, providing insights into future-ready energy management systems.

The growing interdependence between gas and electricity networks, coupled with the increasing adoption of heat electrification, presents significant challenges for energy system reliability and sustainability. This paper introduces a novel probabilistic model for integrated gas and electricity networks, incorporating uncertainty in gas demand, electricity demand, and wind generation using kernel density estimation and copula-based correlation modelling. The model is validated using the Kolmogorov-Smirnov test and applied to assess the impacts of heat electrification on Great Britain’s energy networks under different future scenarios. The results indicate a significant increase in gas consumption for electricity generation by 2040, driven by the reliance on gas-fired power plants to meet peak demand. However, total gas demand declines due to reduced gas boiler usage and increased deployment of heat pumps. The study also highlights a significant rise in overload probability in electricity transmission lines, while the gas network experiences reduced congestion. A sensitivity analysis highlights the importance of considering electricity-gas demand correlation in energy planning. These findings provide critical insights for policymakers and network operators to ensure secure, flexible, and resilient energy infrastructure during the transition to a low-carbon future. The proposed model, made publicly available, offers a scalable framework for further research in integrated energy system planning and operation.

This paper presents a probabilistic method for assessing overload probability of power lines and gas pipelines in integrated gas and electricity networks with the consideration of heat electrification. The proposed model fully considers the correlated uncertainties associated with intermittent generation and fluctuating loads using suitable probability distribution functions and copulas. The effectiveness of the proposed method is verified with application in two test systems: the integrated 9-bus electricity and 8-node gas network, and the integrated IEEE 39-bus and Belgian 20-node gas network. Numerical results show that heat electrification increases the overload probability of some power lines due to a major shift of heat loads to the electricity network. When the correlations of wind and photovoltaic generation as well as electric, gas and heat loads are considered, the overload probability of these power lines further increases showing the importance of correlation modelling for an accurate estimation of power flows and line overload probability. When an uneven distribution of heat loads (i.e., clustering of low-carbon technologies) in the electricity network is considered, the overload probability of some power lines further increases. Heat electrification decreases the overload probability of most gas pipelines, although the probability of overloading a few gas pipelines increases as a result of supplying gas to gas-fired power generators to meet the electrified heat demand.

In light of the increasing environmental challenges and the need for sustainable and circular practices, the adoption of low-carbon technologies (LCTs) is vital. To achieve this transition, it is essential to train a skilled workforce in LCTs aligned with sustainability and circularity principles. This paper investigates the knowledge gaps and discusses emerging trends in this area through bibliometric analysis. This is followed by a systematic roadmap, which aims to facilitate widespread adoption of LCTs through curriculum and faculty development, infrastructure and laboratory investment, industry partnerships and policy engagement. This work contributes valuable insights to the discourse surrounding the broader adoption of LCTs while upholding the principles of sustainability and circularity.

The adoption of multiple energy sources is crucial for achieving a sustainable and resilient energy infrastructure. This calls for the education and training of a skilled workforce versed in multi-energy systems. Traditional classroom instruction, particularly for manual problemsolving, struggles to effectively convey the complexities of multi-energy systems, which hinders student learning in lectures. This paper proposes a teaching approach based on integrating data science notebooks into lectures for preparing engaging and interactive lessons. An example Jupyter notebook and lesson is presented, which examines the impact of different price signals on the energy consumption and costs of traditional and smart buildings, equipped with multiple energy sources. The proposed teaching approach can replace manual exercises and enhance student learning and understanding through interactive problem-solving, bridging the gap between theory and practice.

The installation of heat pumps, powered from clean electricity sources, will play a key role in heat decarbonisation. However, the resulting surge in electricity demand will put the existing transmission lines under enormous strain, increasing the risk of line overloading, potential failure and even loss of load events. This paper presents a probabilistic method for assessing line overload in integrated gas and electricity networks with the consideration of heat electrification. The established model fully considers the uncertainties associated with intermittent generation and fluctuating loads. The effectiveness of the proposed method is verified via its application in an integrated 9-bus electricity and 8-node gas network. Numerical results show that the overload probability of power lines and gas pipelines increases as a result of heat electrification. The overload probability further rises when the correlations of renewable generation and loads are considered, confirming the significance of modelling correlation for an accurate estimation of power and gas flows. The overload probability of some power lines and gas pipelines soar as they become disproportionately loaded, when considering an uneven distribution of heat loads (i.e., clustering of low-carbon technologies) in the electricity network.

Understanding the future of multi-vector energy networks in the context of the transition to net zero and the energy trilemma (energy security, environmental impact and social cost) requires novel interdisciplinary approaches. A variety of challenges regarding systems, plant, physical infrastructure, sources and nature of uncertainties, technological in general and more specifically Information and Communication Technologies requirements, cyber security, big data analytics, innovative business models and markets, policy and societal changes, are critically important to ensure enhanced flexibility and higher resilience, as well as reduced costs of an integrated energy system. Integration of individual energy networks into multi-vector entities opens a number of opportunities, but also presents a number of challenges requiring interdisciplinary perspectives and solutions. Considering drivers like societal evolution, climate change and technology advances, this paper describes the most important aspects which have to be taken into account when designing, planning and operating future multi-vector energy networks. For this purpose, the issues addressing future architecture, infrastructure, interdependencies and interactions of energy network infrastructures are elaborated through a novel interdisciplinary perspective. Aspects related to optimal operation of multi-vector energy networks, implementation of novel technologies, jointly with new concepts and algorithms, are extensively discussed. The role of policy, markets and regulation in facilitating multi-vector energy networks is also reported. Last but not least, the aspects of risks and uncertainties, relevant for secure and optimal operation of future multi-vector energy networks are discussed.

The decarbonisation of heat supply will play a critical role in meeting the emissions reduction target. There is, however, great uncertainty associated with the achievable levels of heat decarbonisation and the optimal heat technology mix, which can have serious implications for the future electricity and gas demand. This work employs an integrated gas, electricity and heat supply model to quantify the impacts of heat decarbonisation pathways on the future electricity and gas demand. A case study in the Great Britain is performed considering two heat decarbonisation scenarios in 2050: one is the predominantly electrified heat supply and the other is the predominantly hydrogen-based heat supply. The electricity demand becomes more volatile in the electrified heat scenario as the peak surges to 107.3 GW compared to 51.1 GW in the 2018 reference scenario, while the peak in hydrogen-based heat scenario is 78.4 GW. The peak gas demand declines from 247.6 GW for 2018 to 81.7 GW for electrified heat scenario and to 85.1 GW for hydrogen-based heat scenario, confirming that the seasonality associated with heat demand is shifting away from the gas network and towards electricity network. Moreover, a sensitivity analysis shows that the future electricity demand is highly sensitive to parameters such as relative heat demand, coefficient of performance of air source heat pumps and share of electricity in hydrogen production. Finally, the application of a load shifting strategy demonstrates that demand-side flexibility has the potential to maintain the electricity system balance and minimise the generation and network infrastructure requirements arising from heat electrification. While the case study presented in this paper is based on the Great Britain, the findings regarding the future electricity and gas demand are relevant for the global energy transition. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

In addition to becoming increasingly interconnected and interdependent, energy networks are facing more uncertainties associated with the rapid penetration of intermittent renewable generators as well as the electrification of heat and transport. A suitable probabilistic framework is necessary to account for these uncertainties and the associated risks to energy supply security. This work identifies the most critical input uncertainties affecting the electricity system adequacy in integrated gas and electricity networks using various sensitivity analysis techniques (one-at-a-time, correlation-constrained Morris and Spearman correlation). A case-study is carried out using three test cases with growing renewable penetrations in an integrated 9-bus electricity and 8-node gas network, where the input uncertainties include wind power, photovoltaic power, electric loads and gas loads. Results show that the most critical uncertainties vary between electric loads and wind power depending on the renewable penetrations. Compared to the Spearman correlation method, the correlation-constrained Morris method provides identical results but with considerably reduced computational time.

This paper investigates the impacts of increasing levels of electric vehicle (EV) penetration in an urban distribution network of Surabaya, Indonesia. Surabaya is the second most populous city in Indonesia where the network is operated by national electric company of Indonesia (PT PLN Disjatim) The investigation is conducted under concentrated and distributed EV penetration (each with 10% and 20% penetration calculated as a percentage of residential load). Loads are modeled as dependent variable of the hour selection within the day and added with a normally distributed uncertainty on each of selected load value to better replicate uncertainty within the daily loading. Whereas, EV charging pattern is modelled as probabilistic uncertain sources using Markov Chain Monte Carlo to better replicate the practical condition. Voltage at each node and aggregate losses are employed to quantify and contextualize the impact. Maximum EV penetration where it violates the statutory threshold is also explored in this paper. Simulation result shows that Surabaya distribution network able to withstand EV penetration in both scenarios with the maximum EV penetration is almost doubled than the highest penetration level from simulated cases.

This chapter presents a scenario-based stochastic model for the multistage joint distribution system expansion planning (DSEP) with distributed (micro-) generation and electric vehicle charging stations (EVCSs), taking into account the associated uncertainties. The uncertainty modeling methods found in literature can be classified as stochastic optimization, robust optimization, Monte Carlo simulation, Latin hypercube sampling technique, Taguchi's orthogonal array testing, and probability statistical methods. Different from these methods, a scenario matrix, based on the heuristic moment matching method, is utilized to characterize the stochastic features and correlation among historical wind and photovoltaic generation and conventional loads and EV demands. The historical EV charging demand data are projected using the Markovian analysis of EV driving patterns and charging demand. The scenario matrix is then incorporated into the formulation of the expansion planning framework that aims at minimizing the investment and operational costs. The planning solution outlines the optimal construction/reinforcement of substations, EVCSs, and feeders, along with the placement of wind and photovoltaic generators and capacitor banks over the three-stage planning horizon. The effectiveness of the scenario-based model is assessed through case studies in the 18-bus and IEEE 123-bus distribution systems. Test results obtained in the 18-bus distribution system demonstrate the effectiveness of scenario-based DSEP in terms of minimizing the total investment and operational costs. Moreover the comparative analysis of scenario-based DSEP against the deterministic and robust approaches confirms its superiority in dealing with the uncertainties. Lastly the scalability of scenario-based DSEP is confirmed using the IEEE 123-bus distribution system.

Due to the associated uncertainties, the large-scale deployment of electric vehicles (EVs) and renewable distributed generation is a major challenge faced by the modern distribution systems. The first part of this two-paper series proposes a scenario-based stochastic model for the multistage joint reinforcement planning of the distribution systems and the electric vehicle charging stations (EVCSs). The historical EV charging demand is first determined using the Markovian analysis of EV driving patterns and charging demand. A scenario matrix, based on the heuristic moment matching method, is then generated to characterize the stochastic features and correlation among historical wind and photovoltaic generation, and conventional loads and EV demands. The scenario matrix is then utilized to formulate the expansion planning framework, aiming at the minimization of the investment and operational costs. The proposed expansion plan determines the optimal construction/reinforcement of substations, EVCSs, and feeders, in addition to the placement of wind and photovoltaic generators, and capacitor banks over the multi-stage planning horizon. In the second companion paper, the effectiveness and scalability of the proposed model is assessed through case studies in the 18-bus and the IEEE 123-bus distribution systems, respectively.

This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-to-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.

The second part of this two-paper series discusses two case-studies to confirm the effectiveness of the scenario-based stochastic model developed in the first part for the multistage joint expansion planning of distribution systems and electric vehicle charging stations. Numerical results obtained in the 18-bus distribution system confirm the effectiveness of proposed approach through the minimization of total investment and operational costs, and the adequate characterization of uncertainties. The proposed solution determines the optimal construction/reinforcement of substations, electric vehicle charging stations and feeders, and the placement of distributed generators and capacitor banks along the three-stage planning horizon. The comparative analysis of proposed approach, against the deterministic and robust approaches; confirms its superiority in dealing with the uncertainties of wind and photovoltaic generation, and conventional loads and electric vehicle demands. Finally, the scalability of proposed approach is confirmed using the IEEE 123-bus distribution system.

This paper presents the application of a probabilistic multi-stability assessment of a modified two-area system under the presence of low and high uncertainty sources. The stability of the network is assessed under four stability regimes: frequency, small-signal rotor angle, large-signal rotor angle, and long-term voltage. The probabilistic assessment is carried out using Monte Carlo simulation. Two cases considering low and high uncertainty are investigated. The obtained results are presented in the form of parallel coordinate plots so that the interaction between multiple stability regimes can be more easily understood. It is observed that the poor response of small-signal rotor angle stability generally corresponds to poor response of other stability types in low uncertainty case. However, once the level of uncertainty increases and more sources of uncertainty exist, this relationship is significantly changed.

•The framework and features of the active distribution network planning are described.•The state-of-the-art uncertainty modelling techniques are critically reviewed.•A set of recommendations are proposed to overcome the existing challenges.

The rising penetration of intermittent renewable distributed generation leads to uncertainties in the planning of electric distribution networks. Fully considering the uncertainties pertinent to wind power generation, photovoltaic power generation and load demand, this paper proposes a scenario-based model for the planning of active distribution systems. The solution obtains the optimal capacities and locations of wind and photovoltaic based distributed generators in the distribution system, whilst minimizing the active and reactive power losses as well as voltage deviation. A scenario matrix is generated using the heuristic moment matching technique that captures the stochastic moments and correlation among historical wind and photovoltaic power, and electricity demand. The scenario matrix is then incorporated to propose a stochastic planning model that considers a multi-objective index for minimizing power losses and voltage deviation. Finally, the effectiveness of the proposed planning model is confirmed using case-studies in 53-bus and IEEE 123-bus distribution systems.

Multi-energy microgrids provide a flexible solution for the utilization of the distributed energy resources in order to meet the electrical, heating and cooling energy demands in the off-grid communities. However, the planning of the multi-energy microgrids is a non-trivial problem due to the complex energy flows between the sources and the loads pertaining to the electrical, heating and cooling energy, along with the intermittency of the renewable distributed generation. This work proposes a scenario-based stochastic multi-energy microgrid investment planning model that aims to minimize the investment and operation costs as well as the Carbon dioxide emissions by determining the optimal distributed energy resource mix, siting and sizing in the isolated microgrids. The proposed planning model employs the power flow and heat transfer equations to explicitly model the energy flows between electrical, heating and cooling energy sources and loads. Moreover, an uncertainty matrix is employed to tackle the operational uncertainties associated with the wind and photovoltaic generation, and the electrical, heating and cooling loads. The uncertainty matrix is modeled using the heuristic moment matching method that effectively captures the stochastic moments and correlation among the historical scenarios. The numerical results obtained from the case-study in the 19-bus microgrid test system confirm that the proposed methodology provides significant reductions in the investment and operation costs as well as the Carbon dioxide emissions. Finally, the superiority of the proposed planning solution is also validated using the deterministic planning solution as the comparison benchmark.

Small-scale renewable distributed generation (DG) with different forms together with storage units are now gradually adopted in the premise of households, which enables the residents to promote the level of response at the demand side actively. This chapter explores the optimal energy dispatch problem in the scope of residential community with penetration of renewable DGs and energy storage in the presence of real-time pricing (RTP). An efficient algorithmic solution is presented and implemented at two levels: optimal control within individual households (i.e., managing schedulable loads and storage to minimize electricity purchase cost in 1-day-ahead dispatch with predicted RTP information), and energy trading among neighboring households (i.e., excess energy is re-dispatched and traded across multiple households). This work aims to exploit the potential economical benefits of coordinating the renewable DGs, distributed storage, and domestic loads by using RTP as a leveraging tool to reduce peak demand and minimize the electricity purchase cost while significantly improving the global utilization efficiency of network resources. The performance of the suggested solution is evaluated through a set of simulation experiments for a residential community with 200 households and the numerical result demonstrates direct effectiveness and benefits. Its robustness is further assessed in the presence of prediction inaccuracy of DG generation and RTP.