The increasing aggregation of renewable-based distributed generating units besides the impressive growing usage of non-linear loads raises unwanted challenges for traditional power terms definition in power engineering. This fact consequently affected the performance of the conventional control frameworks and industrial compensation techniques. In this study, the authors aim to provide an insightful summary over the most recognised time domain-based instantaneous power theories and discuss their advantages and disadvantages within a comprehensive mathematical-conceptual and applicational framework for professionals who are using instantaneous power theories within the smart grid applications. They conclude that there is still a need for a modified power theory which can be validated under non-sinusoidal-unbalanced load/source conditions respecting the physical meaning of different power and current components.

The growing trend of distributed generation, such as solar photovoltaic (PV) systems and small scale wind turbines have promoted the development of microgrids which are highly dependent on renewable energy. Due to the intermittent nature of renewable energy, these microgrids are generally equipped with energy storage, such as batteries. Batteries are generally operated using fixed control methods, often deviating from the optimal operation. This aspect has created an opportunity to gain improved outcomes for microgrid owners and operators. This research study describes a pathway for designing an optimisation framework which can be used to optimise the charge and discharge operation of battery storage within a microgrid containing a solar PV system. Optimisation is implemented in terms of gaining maximum cost benefit for microgrid owners. The advantages of using model predictive control optimisation compared to fixed control methods for this particular problem, solvers and verification procedures are highlighted. A case study is provided with results including analysis of battery operation, energy usage, and impact on overall tariff. The study describes each step of the control and optimisation platform development ensuring readers to be able to replicate the process utilised.

A dissipating energy-based technique is proposed to locate the source of forced oscillations (FOs) in power systems. The network and load information is incorporated into the developed algorithm and continuously updated using supervisory control and data acquisition (SCADA) measurement. The effect of electromechanical damping on system response in FO scenario is discussed; and therefore, the efficiency of the proposed technique to locate the source has been investigated. The proposed methodology is tested and verified for different simulation test cases and for different scenario viz. for single and multiple sources of disturbances. In the case of multiple sources of disturbances with different time of initiation of the disturbance, the proposed technique successfully locates all sources in their time durations of disturbance. Different load models have been evaluated for their impact on the success of the proposed algorithm in a real-time digital simulation environment. The proposed technique is successfully verified for the test cases reported by the IEEE PES Task Force on Oscillation Source Location.

During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.

This study proposes a novel multi-agent method for electric vehicle (EV) owners who will take part in the electricity market. Each EV is considered as an agent, and all the EVs have vehicle-to-grid capability. These agents aim to minimise the charging cost and to increase the privacy of EV owners due to omitting the aggregator role in the system. Each agent has two independent decision cores for buying and selling energy. These cores are developed based on a reinforcement learning (RL) algorithm, i.e. Q-learning algorithm, due to its high efficiency and appropriate performance in multi-agent methods. Based on the proposed method, agents can buy and sell energy with the cost minimisation goal, while they should always have enough energy for the trip, considering the uncertain behaviours of EV owners. Numeric simulations on an illustrative example with one agent and a testing system with 500 agents demonstrate the effectiveness of the proposed method.

Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high-performing, reliable, and resilient power system. Three network design formulations based on mixed-integer linear and non-linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much-improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality-of-service in the event of an outage. Analysis of multi-period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.

Recently, resilience studies have become an indispensable tool for sustainable operation of energy infrastructure. In line with the need, this study presents a mathematical model to enhance resilience level of power distribution systems against natural disasters. The model is designed as a three-stage algorithm according to system operators’ actions. The first stage schedules pre-event actions. At this stage, forecasts about the approaching disaster as well as fragility curves of system components are used to identify failure probability of system components. The failure probabilities are used to trip out the lines as much as possible to defensively operate the distribution network, and advantages of alternatives such as distributed energy resources and normally-open switches are taken to serve critical loads. The second stage is to monitor system operating conditions during the event and identify the status of system components. The third stage mainly focuses on scheduling post-event actions. At this stage, based on real data about different elements of the network, available alternatives are taken to restore as much critical load as possible. To evaluate performance of the model, it is applied to a distribution test system and the results are discussed in detail.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

The synchronverter (SV) is currently one of the most promising grid-friendly inverters that mimic synchronous generators (SGs). Almost all the available SV use virtual current signal for synchronization, which require a complicated controller that lacks plug–play capability. This paper proposes a new generation of self-synchronised SV with a simpler controller, which provides plug–play capability with no need for switching between actual controller current and a virtual current for each ON–OFF process. The proposed SV (PSV) can also share the load with other SGs and SVs and also it is able to take part in regulating system frequency and voltage. A linearised small-signal model is derived for the controller to investigate its stability. The PSV is also validated through multiple simulation studies carried out in MATLAB/Simulink environment. The results show the capability of the PSV to achieve self-synchronisation and plug–play operation without a dedicated PLL or a virtual current. Compared to the current self-synchronised SV, the PSV guarantees a smoother grid connection and gives more accurate reactive power sharing. Moreover, the PSV is applied to a modified IEEE 14-bus test system considering three different contingencies to prove its impact on enhancing system dynamics and stability.

The centralised utility-scale photovoltaic (PV) plants installation has greatly enlarged their percentage in the bulk power systems, along with the nature uncertainty for the balance of system power and loads. Consequently, the successful integration of solar PV power in large-scale power systems requires a reliable and efficient multi-area automatic generation control (AGC) system within the control centre. Specifically, area-AGCs that perform tie-line bias control, in which the area frequency regulates the tie-line power flow, must balance the operational control area supply power-and-demand loads within a pre-tuned parameter set. Traditional AGC control systems have area linear controllers that must be periodically tuned to manage the high fluctuation of PV power. A practical two-step tuning method to determine the optimal parameters of existing multi-area AGCs is presented. The proposed method is demonstrated on a five-area multi-machine power system with two large PV plants. The power system was equipped with the synchrophasor-based monitoring system, with a real-time simulation platform serving as the application host. Results indicated that the two-step tuning method provides optimal parameters for all the system AGCs over a wide range of PV penetration levels. Typical results demonstrated the effectiveness of the tuned multi-area AGCs under dynamic conditions and disturbances.

The authors propose an optimal contract mechanism under moral hazard in discrete-time dynamic electric power networks. As the utility (system operator) cannot adjust the control input of the agents (end-users) directly in real time out of respect for individual decision–making, the agents’ control input maximising their own profit does not always maximise social welfare. To avoid the issue, the authors introduce an aggregator as intermediary between the utility and the agents. The aggregator pays compensation for defective ancillary services, which are caused by random disturbance and the agents’ voluntary control. To reduce the compensation risk, the authors first present an optimal incentive/control contract problem for the aggregator's compensation. The problem is usually regarded as a principal-agent problem under moral hazard in contract theory. However, it is generally difficult to solve a contract problem with dynamics expressed as discrete-time simultaneous Bellman equations and a hierarchical control structure as a Stackelberg game. The authors next show that the problem can be solved by regarding it as a linear-exponential-quadratic-Gaussian dynamic game and employing a numerical optimisation technique. Due to the ex-ante appropriate payment contract, the agents select control inputs preferable for the aggregator. The effectiveness of the proposed contract mechanism is finally demonstrated through simulation.

Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP–TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP–TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration.

The invent of advanced metering infrastructure (AMI) opens the door for a comprehensive analysis of consumers consumption patterns including energy theft studies, which were not possible beforehand. This study proposes a fraud detection methodology using data mining techniques such as hierarchical clustering and decision tree classification to identify abnormalities in consumer consumption patterns and further classify the abnormality type into the anomaly, fraud, high or low power consumption based on rule-based learning. The proposed algorithm uses real-time dataset of Nana Kajaliyala village, Gujarat, India. The focus has been on generalizing the algorithm for varied practical cases to make it adaptive towards non-malicious changes in consumer profile. Simultaneously, this study proposes a novel validation technique used for validation, which utilizes predicted profiles to ensure accurate bifurcation between anomaly and theft targets. The result exhibits high detection ratio and low false-positive ratio due to the application of appropriate validation block. The proposed methodology is also investigated from point of view of privacy preservation and is found to be relatively secure owing to low-sampling rates, minimal usage of metadata and communication layer. The proposed algorithm has an edge over state-of-the-art theft detection algorithms in detection accuracy and robustness towards outliers.

This study presents a step toward the grid connection of a wave-energy park through an electric power conversion system (EPCS) developed and installed for the wave-energy harvesting in Lysekil, Sweden. The EPCS comprises a rectifier, a DC bus, and an inverter followed by a harmonic filter (HF). The higher- and lower-order harmonics injected by the inverter in a power quality context are investigated. The lower-order voltage harmonics partially distort the voltage-source inverter output grid current. A phase-locked loop-based (PLL) grid-phase tracking is used to attenuate the lower-order harmonics by reflecting the grid harmonics in the inverter output. An expression for the grid-current harmonics as a function of the grid-voltage harmonics has been derived and implemented. A mathematical model is derived to obtain a transfer function for the PLL, and finally, proportional–integral gains are tuned for stable system operation. An HF for mitigating the higher-order harmonics has been implemented. The total harmonic distortion is evaluated experimentally, and the results fulfil the grid-code requirements at various frequencies and harmonic orders.

The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this study, an enhanced MPP tracking (MPPT) method is proposed utilising the state estimation by the sequential Monte–Carlo (SMC) filtering, which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP to refine the estimation by SMC. Moreover, a quick irradiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change.

Among all converters, one of the most prominent technologies employed in multi-infeed ac/dc (MIACDC) smart grids is the modular multilevel converters (MMCs). The core part of the MIACDC grids is their dc-voltage power port. All MMC's components in a dc-voltage power port – which are capable of significantly impacting on the dynamics – are mathematically modelled in the space-phasor representation using the rotating dq-frame. Afterwards, the effects of each submodule capacitors and arm inductors on the dc-voltage power port's dynamics are investigated and analysed, separately. This paper mathematically shows that the former is affecting the low-frequency range of the bandwidth, and the latter is impacting on the high-frequency one. Moreover, this paper demonstrates that a robust, optimal controller synthesized by the µ-analysis is a good candidate to induce both robust stability and performance in an MMC-based dc-voltage power port. In order to illustrate the contributions of this article, detailed mathematical analyses; comparative results simulated by the switching model of MMC; and experimental results produced by a test rig, which is able to examine the transient performance of an MMC-based dc-voltage power port, are provided. For comparison, the results of the PI-Lead controller and those of another controller optimally synthesized have been provided.

Under-frequency load shedding (UFLS) schemes are designed by specifying a given amount of load to shed at various frequency thresholds to prevent the collapse of the electrical power system in the event of a large generation-load imbalance. An UFLS step is constituted of a group of medium-voltage feeders that trip when a given frequency threshold is reached. This study focuses on the method to be used when allocating a given feeder to a given step. First, the authors introduce performance metrics to quantify the accuracy level with which the UFLS target is met. Second, they model: the allocation method currently used in France; a variant of that method; and a new method introduced in this study, based on an automated clustering technique. Third, based on real consumption patterns measured from a vast area in France, and using the introduced performance metrics, they compare the efficiency of the three described methods. This study is conducted for the current state of loading of the considered distribution network and for a hypothetical situation with an increased share of distribution-side photovoltaic generation. For the chosen performance metrics, they demonstrate that the first two methods provide similar results while the clustering-based method performs remarkably better.

The design, implementation, and testing of a control system for a flexible microgrid (MG) is presented in this study. The MG controllers can be implemented in a real-world MG with multiple smart switches, photovoltaic panel system, and battery energy storage systems (BESSs). With the benefits from smart switches, the MG has unique characteristics such as dynamic boundary and flexible point of interconnection (POI) concepts. To control such a unique MG and realise the dynamic boundary, an MG central controller and two types of local controllers are implemented. Compared to the MG with fixed boundary, the MG with dynamic boundary can have smaller BESS capacity, better utilisation of renewable energy, and multiple POI options. Also, compared with IEEE Std 2030.7–2017, the topology identification and active and reactive power balance functions are newly designed to realise the dynamic boundary concept. The planned islanding and reconnection functions are modified to realise the flexible POI concept. These functions are introduced including the software architecture, cooperation, and interaction among them. Finally, a hardware-in-the-loop testing platform based on the Opal-RT real-time simulator is set up to verify the performance, realisation of the dynamic boundary, and flexible POI concepts with four comprehensive test scenarios.