This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

The increasing penetration of electric vehicles (EVs) brings challenges and opportunities for power systems. One particular opportunity concerns the use of parked EVs to provide energy and associated services to the grid. In this work, the potential energy storage capacity of parking lots (PLs) of EVs is computed using the proposed stochastic model which considers the sporadic nature of the EV’ behaviours (i.e. arrival/departure, battery degradation, travel pattern, charge/discharge rates). The analysis was performed for two types of PLs with very different occupancy distributions, i.e. a shopping centre PL, and a workplace PL. In both cases, the available energy storage capacity of EVs was estimated hourly using real household travel data, i-MiEV data and car park occupancy records. The results show that the aggregated energy storage capacity closely follows the occupancy of EVs in the PLs, and is substantial, with little sensitivity to charging rate. The proposed stochastic modelling considered the variations in energy consumption, battery degradation, and user behaviour, predicted 13.4% less peak capacity than deterministic modelling. Moreover, the authors conclude that the shopping centre PL is a viable energy resource to the grid, with their scale and throughput compensating for the relatively low occupancy.

As electric vehicles (EVs) are currently under-utilised, the features of deploying EVs as distributed energy resources (DERs), based on an EV as a service (EVaaS) framework, are exploited and a resource allocation scheme is proposed for optimum association of dispersed EVs with critical load for demand fulfilment in microgrids. The proposed approach is based on a combined economic emission (CEE) optimisation model where both energy costs and carbon emissions are taken into account. The CEE optimisation problem is then formulated as a bi-objective optimisation problem, considering a number of practical constraints, such as energy demand, cost budget, emission limit and charging station limit. Carbon price is introduced to convert the bi-objective problem into a single objective function. The authors included EV battery degradation cost to ensure EV owners are not worse off after EVaaS participation. The feasibility of the proposed model is demonstrated in simulation studies. The approach has been extended to evaluate the trade-off between EVaaS and conventional DERs. Numerical results demonstrate the efficiency of the proposed resource allocation scheme.

An increasing share of electric vehicles can mean excessive peak loads in low-voltage power distribution networks. Introducing peak shaving mechanisms to the charging systems, such overloads can be mitigated significantly. The first contribution of this study is to quantify the amount of flexibility that electric vehicles can contribute to peak load reduction so that the drivers can still fully charge the batteries of their vehicles. The second contribution is that the study presents and compares two optimisation strategies for peak load reduction. The work is based on real charging data covering about 25,000 charging sessions at various charging sites in the metropolitan area of the Finnish capital city. The main finding is that the peak loads at charging sites can be reduced by up to 55%. Another important result is that load reduction through low-power charging is achievable only if the average parking time at the charging site is >3 h, without affecting the user experience negatively. It is also found out that the average parking time is over 2 h longer than the average charging time, which indicates the enormous potential of electric vehicles in peak shaving.

Fast charging is a promising way for plug-in electric vehicles (PEVs) to get recharged quickly and reduce the impacts of long-lasting charging process on PEV owners’ daily life. Decreasing time during charging PEVs also makes the decision of PEV owners choosing where to charge affected more by the time length of driving towards and waiting in charging stations, raising new requirements for charging facilities planning. In this study, a cost minimisation planning method of PEV fast-charging stations taking influences of queuing and driving time into consideration is proposed and solved by the genetic algorithm-based methodology. An iterative algorithm obtaining the equilibrium of the user's decision of place to charge is proposed to consider the impacts of waiting and driving time at different charging stations on PEV owners. The effectiveness of the proposed strategy is then verified through the case analysis based on trajectory data of taxis in Beijing, which shows that the proposed methodology has good performances in computation. Weight of time costs and investment restrictions such as the number of charging stations would also influence the planning result.

Combined borehole (BH) heat storage systems, batteries and power-to-gas system have the potential to shift load, reduce carbon emissions, provide hydrogen for fuel cell cars and save energy costs for end customers on an extended scale. This study proposes an optimal operation strategy for a local multi-vector energy storage system, which includes batteries, BH thermal storage, the power to the gas system and the fuel cell cars system. These storage systems can be divided into the short-term storage system and inter-seasonal storage system or low capacity storage system and high capacity storage system. The optimisation problem is divided into a two-stage framework, (i) the first stage optimisation is seasonal optimisation, which gives an approximate optimal operation plan for BH heat storage systems in the following year; (ii) the second stage develops a day-ahead robust optimal plan for all storage systems. Finally, the algorithm will return to seasonal optimisation to update the operation plan for BH heat storage systems to make results more accurate. The test case of eight nodes illustrates that the combined energy system of photovoltaic, heat pump power to gas, BH and batteries can provide hydrogen to fuel cell cars and significantly save power costs for customers with the optimal operation.

Voltage control devices are employed in power distribution systems to reduce the power consumption by operating the system closer to the lower acceptable voltage limits; this technique is called conservation voltage reduction (CVR). The different modes of operation for system's legacy devices (with binary control) and new devices (e.g. smart inverters with continuous control) coupled with variable photovoltaic (PV) generation results in voltage fluctuations which makes it challenging to achieve CVR objective. This study presents a two-timescale control of feeder's voltage control devices to achieve CVR that includes: (i) a centralised controller operating in a slower time-scale to coordinate voltage control devices across the feeder; and (ii) local controllers operating in a faster timescale to mitigate voltage fluctuations due to PV variability. The centralised controller utilises a three-phase optimal power flow model to obtain the decision variables for both legacy devices and smart inverters. The local controllers operate smart inverters to minimise voltage fluctuations and restore nodal voltages to their reference values by adjusting the reactive power support. The proposed approach is validated using the IEEE-123 bus (medium-size) and R3-12.47-2 (large-size) feeders. It is demonstrated that the proposed approach is effective in achieving the CVR objective for unbalanced distribution systems.

In this study, virtual excitation emulation (VEE) is proposed along with the virtual inertia emulation (VIE) for a virtual synchronous generator (VSG) in a standalone microgrid to improve the AC voltage and frequency profile through the control of power electronic DC–AC converter in the microgrid. The VIE emulates the inertia and damping characteristics of a synchronous generator by mimicking the swing equation. The VEE is proposed to emulate the behaviour of the field circuit and exciter system of a synchronous generator. In addition, a restoration control is also added with the inertia and excitation emulation to restore the magnitude and frequency of the output voltage at the rated value and achieve isochronous control. A linearised small-signal model of the VSG with the proposed control technique is also presented to analyse the system stability and design the control parameters. The standalone microgrid is simulated in MATLAB/Simulink environment to show the effectiveness of the proposed control technique. From the simulation results, it is reflected that the proposed control technique improves the voltage and frequency response during transient and steady-state including the frequency nadir of the microgrid.

The increasing deployment of fast charging infrastructure is coupling the operation of power and transportation systems. However, how to evaluate the coupling relationship between these two systems is less studied. This study proposes a look-ahead decentralised framework to solve the integrated power-traffic flow problem using the optimality condition decomposition (OCD) technique. By exploring the similarity between the iterative procedure of the algorithm and the interactive decision-making process of the systems, this study adds two original contributions to this literature. First, the authors show that the convergence performance of the OCD algorithms is closely related to the interdependency between the power and transportation systems. Numerical oscillations or failure of convergence could occur in highly interdependent systems. This can be used as a metric for assessing whether the coordinated operation of the two systems is essential. Second, they employ a dynamic multiplier-based OCD algorithm to solve the integrated flow problem in a decentralised manner with improved convergence performance. Their proposed algorithm considers the sensitivity of the electricity prices to the charging demand, which reduces the price fluctuations at some congested electrical nodes to enhance the convergence speed. The case study demonstrates factors that influence the coupling relationship between the power and transportation systems.

In the current scenario of the deregulated Indian electricity market where the power demand and its availability vary remarkably, the factors playing a significant role in demand variations are often associated with the impact of unprecedented weather conditions and technological evolutions. To maintain grid security and discipline that yield to financial implications, there lies a great need to formulate an equilibrium between electricity supply and demand. Devising a model to anticipate the variations which are highly adaptive to such changes is the need of the hour. For this purpose, an algorithm has been proposed in this study, which is best suited for the day-ahead load forecast. The variables selected for the forecast are one-day-lagged demand statistics, seasonality trend, weather, and calendar variables. The proposed algorithm outperforms the existing benchmark model, which is evaluated through various statistical performance metrics such as mean absolute percentage error, mean absolute error, root-mean-square error, and coefficient of variation. The performance of the proposed methodology at the seasonal level is analysed and validated through uncertainty analysis with one post-sample year for the state of Delhi, India. This model presents its compatibility to prevalent grid regulations as well as shall hold good in the weather and demand variations possibly expected in the future.

Peer-to-peer (P2P) energy market has emerged as a promising way to absorb local generations. However, unregulated P2P transactions are likely to exacerbate voltage violations at distribution networks. The challenge is how to ensure P2P markets to flourish whilst maintaining system voltages within the stator limits. This study proposes a distributed-hierarchical control structure consisting of a central controller and peer controllers to address the challenge. The central controller computes the optimised P2P transaction levels and the nodal voltage references simultaneously and sends the outcomes to the peers as routing-update messages at regular intervals. The peer controllers are developed based on individual phase decupled P–Q theory where two individual channels follow the optimised transaction levels and nodal voltage references. The peers, also, apply a current limit strategy to release the network capacity and improve voltage profiles when confronting with short-term voltage magnitude variations between the two update intervals. A case-study-based investigation shows that reactive power contributions improve the power transaction levels up to 30% while the node voltage violations are reduced. The effectiveness of the proposed strategy is validated using simulation and compared with state-of-the-art voltage mitigation methodologies over the IEEE 19-bus system.

In this study, networks of interconnected heterogeneous micro-grids are studied. The transient dynamics is modelled as an averaging process whereby micro-grids are assimilated to dynamic agents in a network. An analysis of the convergence of the consensus dynamics is carried out under different assumptions on the damping and inertia parameters and the topology of the network. This study provides an insight into the relation between the network topology and the system's response. An analysis of the ways in which the heterogeneous inertial parameters affect the transient response of the network is also implemented. Additionally, the conditions that guarantee stability are identified when the system is under the influence of uncertain non-linear parameters. Finally, simulations are carried out based on a model calibrated on an existing network in the UK under parameter uncertainties.

In this study, the problem of an electric vehicle (EV) aggregator participating in a three-settlement pool-based market is presented. In addition to energy procurement, it is assumed that EVs can sell electricity back to the markets. In order to obtain optimised solutions, the aggregator is considered as a price-maker agent who tries to minimise the cost of purchasing energy from the markets by offering price-energy bids in the day-ahead market and only energy bids in both adjustment and balancing markets. Since the problem is heavily constrained by equality constraints, the number of binary variables for a 24-hour market horizon is too large which leads to intractability when solved by traditional mathematical algorithms like the interior point. Therefore, an evolutionary metaheuristic algorithm based on genetic algorithms (GAs) is proposed to deal with the intractability. In this regard, first, the stochastic problem is formulated as a mixed-integer linear programming problem, and as a non-linear programming problem to be solved by CPLEX and GA, respectively. The former is used to ensure that the GA is tuned properly, and helps to avoid converging to local extremums. Furthermore, the solutions of the two formulations are compared in simulations to demonstrate GA could be faster in obtaining better results.

Uncertainties in the solar photovoltaic (PV) power generation, random behaviour of consumer load power demand, and unexpected failures are the major factors for the consumer power interruption (CPI) hours, which reduce the reliable power supply rate or reliability index, of an islanded microgrid (IMG). One way to address this challenge is to complement the IMG with solar PV, energy storage system (ESS), and diesel generator (DG). This study first defines the reliability index of an IMG that equipped with solar PV, ESS, and DG. Then the authors propose an energy management strategy (EMS) for an IMG to maintain the reliability index above a given threshold limit while lowering the cost through higher utilisation of PV, lower CPI hours, and lower DG operating hours. They utilise real IMG consumers load power demand and solar radiance data for simulation studies to show how the proposed EMS achieves the desired trade-off in reliability and cost.

DC microgrid provides the horizontal infrastructures to integrate distributed generation (DG) and loads. Unlike traditional AC systems, DC systems cannot survive or sustain high magnitude fault currents. It makes locating faults very difficult. The conventional protection techniques completely de-energies the DC link in the DC microgrid. A new protection scheme for multi-terminal DC microgrid against line-to-line fault and the low resistance earth fault is presented in this study. The scheme isolates the faulted section from the DC microgrid. Healthy sections are operated without any disturbance and supply continuity is maintained in a ring main DC bus system. The current sensors are mounted at DC bus segments to monitor the entering and outgoing current at different nodes. Further, the current sensors are also mounted at both ends of service mains to monitor their current difference at both ends of the service mains. The controller detects this current difference and opens circuit breakers. To meet the requirement of fast interrupting time and high short-circuit current withstanding capability, insulated-gate bipolar transistors used as circuit breakers. The fault location scheme gives the fault location in various sections (service mains) and faults resistance in the microgrid. The proposed concepts have been verified by computer simulation.

Hybrid battery energy storage system (HBESS) consists of high power density battery and high energy density battery will have a bright future in special isolated DC microgrid conditions such as the all-electric ships and all-electric airplanes, which have strict limitation on storage capacity and size. In this study, a new decentralised control strategy based on mixed droop is proposed to HBESSs with considering the batteries. In decentralised control strategy, conventional V–I droop controller is utilised to high energy density battery to mainly supply the steady power, I–V droop controller is utilised to high power density battery to respond to power change and supply a few steady power. In addition, dynamical state-of-charge (SoC) regulation algorithm is utilised to reassign the battery power according to their own SoC. The power coordination of the high energy density batteries and high discharge rate batteries is achieved by adjusting the values virtual impedance and reference input voltage. Case study shows that the proposed control strategy is flexible and efficient.

This study formulates a stochastic bi-level optimisation model for an aggregator participating in a two-settlement pool-based market while competing with rival aggregators. The market structure comprises a day-ahead and a balancing market. The upper level problem maximises the profit of the aggregator, while the lower level problem minimises the cost of energy procurement. The novelty of this work is that the aggregator is considered as a price-maker in both markets in which it participates in the day-ahead market by offering energy and price bids, and in the balancing market by offering only energy bids. Therefore, this study formulates an optimisation model where the price-maker economic bidding problem is considered without simplification, and serves as a reference for future studies. The proposed formulation is non-linear due to its bi-level structure and price-maker offers. The problem is then properly linearised into a single-level problem. The concept of conditional value at risk (CVaR) has been applied in the problem formulation to maximise average profit under worst scenarios. Finally, numerical results are presented through an illustrative case study to assess the performance of the proposed model. The results show that the profit of economic bidding is over 100% more than that of self-schedule in low risk cases.

The potential gradient method is proposed for system scalability of pulsed power networks. The pulsed power network is already proposed for the seamless integration of distributed generations. In this network, each power transmission is decomposed into a series of electric pulses located at specified power slots in consecutive time frames synchronized over the network. Since every power transmission path is pre-reserved in this network, distributed generations can transmit their power to individual consumers without conflictions among other paths. In the network operation with a potential gradient method, each power source selects its target consumer that has the maximum potential gradient among others. This gradient equals the division of power demand of the consumer by the distance to its location. Since each of the target consumer selection is shared by power routers within the power transmission path, the processing load of each system component is kept reasonable regardless of the network volume. In addition, a large-scale power grid is autonomously divided into soft clusters, according to the current system status. Owing to these properties, the potential gradient method brings the system scalability on pulsed power networks. Simulation results are described that confirm the performance of soft clustering.

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users’ anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles’ owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs’ batteries during the off-peak hours and drawing it from the EVs’ batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs’ charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This study demonstrates the use of variable-speed refrigeration (VSR) technology, based on brushless DC motors, for the fast-frequency response. First, the authors derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, they consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived through transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full- and suitable reduced-order models, allowing the application of simplified models in large-scale system studies.

The following article, Xiaoou, L., 'Evaluation method for supply capability of multiple energy sources in integrated energy microgrid considering reliability constraints', was withdrawn on the 12th October 2020 at the request of the author and in agreement with the Editor-in-Chief and the Institution of Engineering and Technology. This was because of errors found in the results.