This study presents a review and comparison on the power generation by artificial vortex approach. The basic principles of the artificial vortex theory are presented to comprehend the topic. The main characteristics of the components, working principles and features of the existing Vortex Generator Systems for the production of electricity have been discussed and compared. The literature demonstrates that there are two ways to establish the vortex updraft flow: (i) by constructing a special design of the updraft tower to yield a vertical pressure gradient along the axis of a vortex. These types of system have a high investment cost due to the tall tower, or (ii) by using an external heat source to create large temperature rise in ambient air and enhance the buoyancy forces. This integration between the vortex generation system and the wasted heat source is restricted operation in the industrial areas. Therefore, enhancement and development of the way to use a simple construction design based on the solar energy as a heat source support the technology of electricity production by artificial vortex generation. A solar based vortex power generator could be a potential technology for electric power generation, especially in the desert and rural areas.

In off-grid wind power plants, the uncertainty of net load becomes the main factor that controls the operation and planning of these plants. The term net load refers to system demand minus the generation from variable renewable resources. Energy storage system is a key solution for system operators to provide the required flexibility needed to balance the net load uncertainty. This study proposes a probabilistic approach for sizing a battery storage system (BSS) with the aim of mitigating the net load uncertainty associated with the off-grid wind power plant. A novel battery-sizing index that takes into account the probabilistic nature of the wind resources and the electric load is developed. The proposed sizing approach aims to quantify the required BSS capacity for operating the wind plant without incurring excessive battery installation cost as well as for reducing the mismatch between the wind generation and the electric load. An 8.5 MW utility-scale wind farm is used as a test system to demonstrate the effectiveness of the proposed approach.

An accurate active islanding detection method for grid-tied inverters in distributed generation using phase disturbance based on grid synchronisation is presented in this study. To have good performance on both power quality and islanding detection, a third-harmonic injection is generated by a new kind of phase disturbance and the scope of phase disturbance coefficient is precisely defined, besides, for extracting the third-harmonic component at the point of common coupling accurately, a selective harmonic extraction based on multiple second-order generalised integrators is also provided. Performances of the proposed method have been studied and confirmed through extensive simulations using MATLAB/Simulink platform.

The authors analyse the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting. A mathematical model based on stochastic dynamic programming is used to compute the water values for the system considering different power plant configurations. The uncertainties in inflow and both energy and reserve capacity prices are considered through a discrete Markov chain. Subsequently, the system operation is simulated based on the obtained water values to assess system performance and expected revenues from the two markets. The model is applied in a case study for a Norwegian hydropower producer, showing how the power plant operation changes and profitability increases when considering sale of reserve capacity. The authors emphasise on how the water values are influenced by the opportunity to sell reserve capacity, and assess how the representation of non-convex relationships in the water value computations as well as simulation influence the profitability.

Owing to critical role of photovoltaic (PV) power in oncoming energy market, an accurate PV power forecasting model is demanded. In this paper, an effective solar power prediction model composed of variational mode decomposition, information-theoretic feature selection, and forecasting engine with high learning capability is proposed. The feature selection method is based on information-theoretic criteria and an optimisation algorithm. The forecasting engine is multilayer perceptron neural network equipped with modified Levenberg–Marquardt learning algorithm. An evolutionary algorithm is also incorporated into the training mechanism of the forecasting engine to enhance its learning capability. Effectiveness of the proposed PV prediction model is illustrated on a Hungarian solar power plant.

High direct solar radiation and the vast desert areas in Iran are the main factors to the attraction in using solar technologies such as floating solar chimney power plant (FSCPP) for electrical and thermal productions of energy. This article studies the performance of floating solar chimney technology for manufacturing purpose. In order to find the best area for the FSCPP, 14 different areas across the country are considered. The results revealed that FSCPP can produce 5–200 MW of electrical energy. Then a study was performed according to the return of finance rate, net price value, and finance return period criterions. The results showed that these power plants are able to be built in large scales of 100 and 200 MW of electricity with the annual capacity of 311 and 641 GW, respectively. However, a comparison between the cost analysis of two tested models shows that the 200 MW power plant with the cost of 14,273.64 Rials is more beneficial.‏

Autonomous voltage security regions (AVSRs) have been proposed to prevent cascading trip faults in wind farms. However, due to insufficient dynamic reactive power (DRP) support and unreasonable DRP allocation in large-scale, centralised wind power integration areas, especially for high wind power penetrations, AVSRs may be too small to guarantee secure wind farm operation, resulting in large amounts of wind power curtailment. DRP allocation is crucial for wind farms to accommodate more wind power and decrease curtailment. Therefore, a DRP allocation scheme based on the AVSR to accommodate more wind power is proposed in this work. An improved AVSR considering wind power curtailment is first derived; the function between the AVSR, DRP reserves and curtailment is then obtained, and an optimal DRP allocation scheme using Benders decomposition is finally proposed. Good performance is observed when tested on a real large-scale wind power base in Northern China.

Incentive-based regulations, toward higher performance networks, are the main driver for minimising losses in distribution systems. On the other hand, more renewable generation is needed to achieve environmental targets. Hence, a multiobjective model is introduced in this study seeking to minimise energy losses as well as maximise renewable generation in radial distribution systems (RDSs). Two alternative control strategies of future smart grids such as reactive power management using adaptive power factor control and coordinated voltage control are considered in the optimisation problem. The problem is subjected to the various technical constraints such as voltage limits, thermal limits and reactive capability limits of photovoltaic (PV) penetration, power factor regulations and underload tap changer adjustment. Also, the uncertainties of load and renewable generation are considered, too. Then, the obtained non-linear programming problem is relaxed and reformulated as a well-suited and computationally efficient second-order cone programming problems. To obtain more efficient and evenly distributed Pareto set to help decision-making process, a modified normal boundary intersection method is introduced for solution methodology. The implementation of the proposed framework on IEEE 33-bus RDSs shows the gains that the flexibility provided by innovative control strategies can have on energy loss reduction and PV capacity.

An estimator based on echo state networks (ESNs) is presented in this study to estimate voltage harmonic distortion waveforms at non-monitored sensitive loads using current and voltage at a monitored location. Since distributed generations such as photovoltaic systems have played a special role in distribution networks so they are considered in this study and their effects on a harmonic voltage waveform estimator are evaluated. Voltage harmonics are considered as the main type of waveform distortion in a power quality approach. Voltage and current harmonics cause an increase in the malfunction of electrical equipment in power systems. To detect and analyse the voltage harmonics, installation of power quality monitors (PQMs) at all buses is not economical. Reducing the cost associated with the monitoring procedure can be achieved by optimising the number of PQMs to be installed. The presented technique is examined on an IEEE 37-bus network and results from the studies indicate an acceptably high accuracy of ESN estimator.

Ambitious target of reaching 100% renewable share of primary energy supply in Nordic countries by year 2050 is analysed by using TIMES-VTT energy system model. Role and feasibility of power-to-gas (P2G) technologies, producing methane (CH₄) gas from surplus electricity, is analysed in context with high levels of wind and solar power. Three scenarios are set with varying constraints for the energy system and with objective to improve conditions for wind and solar power seasoned with P2G utilisation. In BASE scenario, non-renewable energy sources are phased out by a high fuel tax for year 2050. In LO-HH2, the authors exclude hydrogen-based transport and reduce imported biofuels. In LO-BIO, they decrease availability of biomass for energy and advanced biofuel production. Reaching 100% renewable target demands significant changes in energy system, especially in energy production and transport sectors. Transforming primary energy supply to renewable requires high level of electrification, but also bioenergy is essential for Finland and Sweden. Thus, P2G competes with biomass gasification technologies. Volatility of wind and solar power productions can be balanced by international trade. Main drivers for P2G are supply of synthetic gas to transport and industrial sectors, instead of acting as energy storage for surplus electricity.

With respect to rapid growth of offshore wind power, multi-terminal HVDC system is going to be an attractive way of the power transmission for remote and large offshore wind farms. Since the HVDC cables are the vital component of the multi-terminal HVDC transmission system, an accurate and fast fault detection method is necessary for protection and maintaining uninterrupted power delivery to the main grid. This study presents a novel method to diagnose HVDC cable faults and unbalancing of DC capacitor bank in multi-terminal voltage source converter HVDC systems for offshore wind farms. Transient voltage of cable sheath is investigated as a main parameter for detecting types of cable faults. Extensive simulations are performed on EMTDC-PSCAD platform and the results show the effectiveness of the proposed technique.

This study presents a fuzzy-proportional–integral (PI) controller having self-tuning property for current control of doubly fed induction generator (DFIG). Two fuzzy-PI-based controllers: first one is used for controlling rotor current of DFIG, whereas the other come up with optimal output-scaling factor for the former are employed to carry out the algorithm. The proposed controller is robust with different operating conditions and parameter changes because of having adaptive feature. The performance of the control algorithm both transient and steady-state conditions are tested with several experiments performed various operating points. The results achieved from both simulation and experimental tests verify that the implemented method is superior to steady-state and transient response at all operations as exhibits robustness to wind speed and parameter variations.

Increasing penetration of intermittent renewable generators (RGs) results in variable generation. This is likely to create congestion of varying quantum and temporal distribution in distribution networks. The existing distribution network charging methodologies like long-run incremental cost (LRIC) offer location-specific signal to users and charge customers reflecting their use of system. These methodologies can be modified using contribution factor to reflect users demand coincident with peak network demand. RGs are encouraged by relieving them from such contribution-based pricing signals. Congestion caused by intermittent RG could be mitigated by utilising demand customers’ flexibility. This study incorporates short-term demand-side response (DSR) signal for demand customers to manage variability caused by RG in the modified LRIC pricing model. These short-term DSR signals in the form of peak/off-peak charge offers, in conjunction with demand elasticity, helps to assess customer response. This result in modified load profile for various class customers connected at the nodes, which is used for evaluating network charges. The proposed approach is applied to a practical Indian reference network with consideration of distributed generation. Results from modified LRIC pricing model encourage users to change their short-term consumption and help network owner to alleviate congestion and minimise network investment.