In recent two decades, the power systems have confronted with considerable changes such as the power system restructuring, growth of distributed energy sources and renewable energy sources (RESs), and emergence of smart grid concept. One of the common challenges caused by these changes is flexibility necessity of energy resources. One of the best solutions to mitigate this challenge is energy storage systems (ESSs) utilisation. The main question is how to determine size, site, and type of ESSs to maximise their benefits. This study reviews the answers to this question according to the research studies. This study first classifies the studies related to ESS expansion planning into two main categories from the viewpoint of the power system operators and the investors. Next, the first main category is divided into three subcategories: ESS expansion planning in microgrids, distribution networks, and transmission networks. The second main category is classified into two subcategories: ESS expansion planning aim to smooth RESs output power and to maximise profit. In each subcategory the modelling approaches, solving methods, and the results are evaluated. Finally, based on the existing challenges, the future research directions of ESS expansion planning are outlined.

It is an undeniable fact that energy systems all over the world are at the point of a paradigm shift as a need for decarbonisation is eminent and unavoidable. The pressure to decarbonise mounts year after year. Since two thirds of all anthropogenic greenhouse-gas emissions come from the energy sector, decarbonisation is more about reducing emissions in the energy system than any other system in the world. The increased need for decarbonisation has resulted in the increased installation of photovoltaic (PV) and wind systems in countries such as China, India, Germany, Ireland, Denmark, Japan and USA. The increased use of intermittent renewable energy resources introduces a need for advanced methods of planning because traditional planning methods give sub-optimal generation capacity mix when the electric grid is faced with high shares of variable renewable energy resources such as PV and wind. In light of this, this review highlights the major changes in planning methodologies when solving for optimal penetration of generation capacity in systems with high shares of PV and wind. The major highlights are placed on why the methodologies need to evolve as penetration levels of PV and wind increase and further highlight missing issues from the current advanced methods.

Wind energy systems based on doubly fed induction generators (DFIG) are very sensitive to grid disturbances, mainly voltage sags, which can cause rotor and stator overcurrents and rotor overvoltage, resulting in turbines shutdown or even damage to the converter connected to the rotor. Besides, the transmission system operators have been issuing strict grid codes, which require the wind energy conversion system (WECS) to keep operating during grid disturbances and supplying reactive power to the system, if necessary. Due to difficulties to access the offshore DFIGs, avoiding damages and shutdowns in these WECS is paramount. In order to overcome this problem, this article proposes the control of the generator stator flux through a series voltage compensator (SVC) with minimised components. The proposed SVC draws power from the grid through a transformer with a single turn in the primary side (single turn primary transformer) and injects power in the system through an LC filter, without any series transformer. The control of the generator stator flux reduces considerably the stator and rotor current as well as the rotor voltage, allowing the generator to ride-through the grid disturbances.

This study presents a revenue maximisation strategy for market integration of a novel wave energy converter (WEC), part of the Ocean Grazer platform. In particular, the authors evaluate and validate the aforementioned revenue maximisation model predictive control (MPC) strategy through extensive simulations and by checking the underlying assumptions of the strategy implementation. Accordingly, an annual simulation of the MPC strategy is shown, which illustrates seasonality effects; furthermore, a benchmark against a heuristic strategy is presented, followed by analyses of the parameter sensitivity and the assumptions on the control loop information that the MPC receives. These efforts shed some light on the impact of variations of the considered parameters and variables on the total revenue and provide insights to optimally scale the WEC. Lastly, the challenges associated with the deployment of such a strategy are addressed, followed by concluding remarks.

The penetration of wind energy into power systems is steadily increasing; this highlights the importance of operations and maintenance, and specifically the role of condition monitoring. Wind turbine power curves based on supervisory control and data acquisition data provide a cost-effective approach to wind turbine health monitoring. This study proposes a Gaussian process (a non-parametric machine learning approach) based algorithm for condition monitoring. The standard IEC binned power curve together with individual bin probability distributions can be used to identify operational anomalies. The IEC approach can also be modified to create a form of real-time power curve. Both of these approaches will be compared with a Gaussian process model to assess both speed and accuracy of anomaly detection. Significant yaw misalignment, reflecting a yaw control error or fault, results in a loss of power. Such a fault is quite common and early detection is important to prevent loss of power generation. Yaw control error provides a useful case study to demonstrate the effectiveness of the proposed algorithms and allows the advantages and limitations of the proposed methods to be determined.

Linear alternators (LAs) coupled to thermoacoustic engines (TAEs) provide a viable solution to extract energy from a heat source in a variety of applications such as waste heat, energy harvesting, solar thermal and biomass power generation. For the electrical power to be maximised, the acoustic impedances of LA and TAE have to match. This requirement cannot, in general, be met by relying only on the design of the LA, but can be achieved at the control level, by using a fraction of the LA inverter current to create ‘electronic stiffness’ which contributes to the overall stiffness tuning the resonance frequency. The same concept can, in principle, be used to replace part of the mechanical spring stiffness in order to overcome the limitations in the mechanical design, at the expense of an increase in LA and inverter ratings. The impact of electronic stiffness on LA power capability and ratings is analysed here. Two meaningful scenarios are considered in the analysis: the LA derating for resonance frequency tuning and the oversizing when springs are partially replaced by electronic stiffness. The study is supplemented with experiments on a small-scale LA test rig.

In the energy supply and conversion system proposed herein, the following energy demands were considered for a hospital: electricity, sanitary hot water, steam, and cooling. A superstructure representing all options of equipment and energy resources was built, with conventional equipment as well as more complex technologies, such as absorption chillers and cogeneration modules. Two renewable energy resources were available: solar photovoltaic energy and biomass (sugarcane bagasse). The solution of a mathematical model based on mixed integer linear programming provided the optimal economic solution, constituted by the configuration of the system (equipment installed) and its operation strategy (how to operate each equipment, throughout one operational year). The objective function considered the minimisation of total annual costs, which encompassed fixed costs (equipment) and variable costs (maintenance and energy costs). A reference system was designed, where only conventional equipment was utilised (no cogeneration, no solar or biomass utilities available). The optimal economic solution included the utilisation of biomass to produce hot water and steam, with an annual cost that was 11% lower than the reference solution. Although the economic optimal solution did not install cogeneration modules, it took advantage of solar and biomass resources to achieve annual minimum cost.

The grid feeding and grid forming inverters are important candidates in grid connected and islanded modes (IMs) of microgrid system, respectively. The grid feeding inverter supplies the preset power during grid-connected mode and the grid forming inverter generates a reference voltage for other distributed generation (DG) interfacing inverters in IM. In such scenario, the proper control of these inverters is an utmost importance. Generally, a nested loop control strategy uses a conventional PI controller for the inner and outer loops of grid feeding and grid forming inverter, but it gives deteriorative performance under parameter uncertainty condition. This study proposes a robust nested loop control scheme with the mixed optimal controller in the outer voltage control loop and the robust linear quadratic regulator (LQR) state feedback controller in the inner current control loop for the smooth operation of the system in both modes. The simulation and experimental results of the DG unit with proposed control scheme demonstrate good stability and performance robustness under the parameter uncertainty. Also, superior transient and steady-state performance are confirmed over the conventional controller.

This study proposes a power electronics interface (PEI) for photovoltaic (PV) applications with a wide range of ancillary services. As the penetration of distributed generation systems is booming, the PEI for renewable energy sources should be capable of providing ancillary services such as reactive power compensation and low-voltage ride through (LVRT). This study proposes a robust model predictive-based control strategy for grid-tied Z-source inverters (ZSIs) for PV applications with LVRT capability. The proposed system has two operation modes: normal grid condition and grid fault condition modes. In normal grid condition mode, the maximum available power from the PV panels is injected into the grid. In this mode, the system can provide reactive power compensation as a power conditioning unit for ancillary services from DG systems to main ac grid. In case of grid faults, the proposed system changes the behaviour of reactive power injection into the grid for LVRT operation according to the grid requirements. Thus, the proposed controller for ZSI is taking into account both the power quality issues and reactive power injection under abnormal grid conditions. The proposed system operation is verified experimentally, the results demonstrate fast dynamic response, small tracking error in steady-state, and simple control scheme.

Grid complexity is increasing progressively as the deepening penetration of renewable power generation and unpredictable demand, which necessitates an exhaustive assessment of system parameters in a probabilistic manner. In this study, the authors employ a dimensional reduction integral method to tackle the above problems challenged by dimensionality. Their approach transforms the multivariate raw moments into a linear sum of several one-dimensional integrals, which could be solved by Gauss quadrature. To handle the correlation between non-Gaussian input variables, Nataf transformation is used to map the inputs into the independent normal domain. Instead of commonly used series expansion such as A-type Gram–Charlier, Edgeworth or Cornish–Fisher, the probability distributions of output variables can be better approximated by C-type Gram–Charlier series with the calculated moments. The salient feature of the proposed method is demonstrated in a modified IEEE 118-bus test system with respect to both accuracy and run times.

This study proposes a coordination of load frequency control (LFC) and superconducting magnetic energy storage (SMES) technology (i.e. auxiliary LFC) using a new optimal PID controller-based moth swarm algorithm (MSA) in Egyptian Power System (EPS) considering high wind power penetration (HWPP) (as a future planning of the EPS). This strategy is proposed for compensating the EPS frequency deviation, preventing the conventional generators from exceeding their power ratings during load disturbances, and mitigating the power fluctuations from wind power plants. To prove the effectiveness of the proposed coordinated control strategy, the EPS considering HWPP was tested by the MATLAB/SIMULINK simulation. The convention generation system of the EPS is decomposed into three dynamics subsystems; hydro, reheat and non-reheat power plants. Moreover, the physical constraints of the governors and turbines such as generation rate constraints of power plants and speed governor dead band (i.e. backlash) are taking into consideration. The results reveal the superior robustness of the proposed coordination against all scenarios of different load profiles, and system uncertainties in the EPS considering HWPP. Moreover, the results have been confirmed by comparing it with both; the optimal LFC with/without the effect of conventional SMES, which without modifying the input control signal.

This study presents a simulation of low-carbon electricity supply for Australia, contributing new knowledge by demonstrating the benefits of load shifting in residential buildings for downsizing renewable electricity grids comprising wind, hydro, biomass, and solar resources. The load-shifting potential for the whole of Australia is estimated, based on air-conditioner load data and an insulation model for residential buildings. Load shifting is applied to enable transferring residential air-conditioner load from peak to off-peak periods, assuming that air-conditioners can be turned-on a few hours ahead of need, during periods where demand is low and renewable resource availability is high, and turned-off during periods of high demand and low resource availability. Thus, load shifting can effectively reduce installed capacity requirements in renewable electricity grids. For 1 h load shifting of residential air-conditioners, Australian electricity demand can be met at the current reliability standards by 130 GW installed capacity, at cost around 12.5 ¢/kWh, and a capacity factor of 32%. The installed capacity can be further reduced by increasing the number of hours that loads can be shifted. The findings suggest that the application of load shifting in residential buildings can play a significant role for power networks with high renewable energy penetration.