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In this paper, risk-constrained arbitrage trading strategies that exploit price differences arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday (CID) and balancing (BAL) markets, are developed and evaluated. To open initial DAM positions, a rule-based trading policy using DAM and CID price forecasts is proposed. DAM prices are predicted using both technical indicator features and data augmentation methods, such as autoencoders and generative adversarial networks. Meanwhile, CID prices are predicted using novel features that are engineered from the limit order book. Using the forecasts, the direction of price movements is correctly predicted the majority of the time. To manage open DAM positions while optimising the risk-reward ratio, deep reinforcement learning agents trained using the advantage actor–critic algorithm (A2C) are employed. Evaluated across Dutch short-term markets, A2C yields profits surpassing those obtained using A3C and other benchmarks. We expect our study to benefit electricity traders and researchers who seek to develop state-of-art intelligent trading strategies.

Contrary to traditional electrolysers which operate continuously at their nominal load, future alkaline electrolysers need to be able to operate over a wide load range due to the variability of renewable electricity supply. We have investigated how the residual ripples from thyristor-based power supplies are influenced by the operating load of the system, and how these ripples affect the efficiency of alkaline electrolysers. For this, a simulation tool was developed which combines a six-pulse bridge thyristor rectifier model with closed-loop current control and semi-empirical electrolysis models. The electrolysis models can simulate the potential response to both direct and high amplitude alternating currents for lab-scale and industrial electrolysers. The electrolysis model of the lab-scale electrolyser was validated with experiments with a square wave current input. The models show that without filters the ripples result in a total system efficiency loss of 1.2–2.5% at full load and of 5.6–10.6% at a part load of 20% depending on the type of electrolyser. The implementation of an optimized L-filter suppresses residual ripples and reduces the efficiency losses to 0.5%–0.8% at full load and to 0.8–1.2% at the minimum load.

The rapid increase in the number of PV installations in current low voltage (LV) distribution networks brings many technical operational challenges. This claims for the deployment of control strategies to deal with these concerns, especially those related to overvoltage issues. Based on this, this paper presents a comprehensive assessment of the performance of PV inverters operating with droop control for overvoltage mitigation using a stochastic methodology based on a Monte Carlo approach. The uncertainty related to the PV generation and the users’ consumption behavior is fully considered through advanced statistical modeling techniques. Voltage magnitude and loading indexes are used as key metrics to assess the technical performance of the distribution network, simulated using OpenDSS, under two droop-based control strategies: Active Power Control (APC) and coordinated Reactive and Active Power Control (RPC-APC). The effects of curtailed energy on the PV users’ revenue is also analyzed. A case of study based on real smart meter data from The Netherlands is used. According to the obtained results, both control strategies are effective to mitigate voltage violations. Nevertheless, for the case of 100% PV penetration, the droop-based coordinated RPC-APC allowed an 18% more of exported energy than the droop-based APC control strategy.

In this paper, we focus on statistical arbitrage trading opportunities involving the continuous exploitation of price differences arising during an intraday trading period with the option of closing positions on the balancing market. We aim to maximise the reward–risk ratio of an autonomous trading strategy. To find an optimal trading policy, we propose utilising the asynchronous advantage actor–critic (A3C) algorithm, a deep reinforcement learning method, with function approximators of two-headed shared deep neural networks. We enforce a risk-constrained trading strategy by limiting the maximum allowed position, and conduct state engineering and selection processes. We introduce a novel reward function and goal-based exploration, i.e. behaviour cloning. Our methodology is evaluated on a case study using the limit order book of the European single intraday coupled market (SIDC) available for the Dutch market area. The majority of hourly products on the test set return a profit. We expect our study to benefit electricity traders, renewable electricity producers and researchers who seek to implement state-of-art intelligent trading strategies.

Inflexible combined heat and power (CHP) plants and uncertain wind power production result in excess power in distribution networks, which leads to inverse power flow challenging grid operations. Power-to-X facilities such as electrolysers and electric boilers can offer extra flexibility to the integrated energy system. In this regard, we aim to jointly determine the optimal Power-to-X facility sizing and integrated energy system operations in this study. To account for wind power uncertainties, a distributionally robust chance-constrained model is developed to characterize wind power uncertainties using ambiguity sets. Linear decision rules are applied to analytically express real-time recourse actions when uncertainties are exposed, which allows the propagation of wind power uncertainties to gas and heat systems. Accordingly, the developed three-stage distributionally robust chance-constrained model is converted into a computationally tractable single-stage mixed-integer conic model. A case study validates the effectiveness of introducing the electrolyser and electric boiler into the integrated energy system, with respect to the decreased system cost, expanded CHP plant flexibility and reduced inverse power flow. The developed distributionally robust optimization model exhibits better effectiveness and robustness compared to a chance-constrained optimization model assuming wind forecast errors follow Gaussian distribution. Detailed profit analysis reveals that although the overall system cost is minimized, the profit is distributed unevenly across various stakeholders in the system. The profit mainly falls with the wind power plants, which therefore are most motivated to make investments in the flexibility resources. Other parties rely on additional policies such as bilateral contracts with the wind power plants to gain incentives to invest. The findings from this study can be used to motivate policy-makers to make proper regulations to incentivize investments in flexibility resources and establish a more reliable power grid.

The cycle aging of a commercial 18650 lithium-ion battery with graphite anode and lithium nickel manganese cobalt (NMC) oxide-based cathode at defined operating conditions is studied by regular electrochemical characterization, electrochemical impedance spectroscopy (EIS) and post-mortem analysis. The study finds that capacity fade, impedance rise and the end-of-life of cycled cells strongly depend on the operating conditions like temperature, current rate, depth-of-discharge and mean state-of-charge. In general, the capacity fade is characterized by a slow linear decrease at first, followed by a rapid decrease. This transition point is found to correlate well to the rate of solid electrolyte interphase (SEI) resistance growth at the anode. A longer lifetime is found for cells cycled at 45 °C than at 20 °C for the same depth-of-discharge and C-rate. Effect of cycle depth on capacity fade is related to the graphite electrode volume changes and the local electrochemical potential at the electrodes. Cells cycling upto 4.2 V are found to have longer linear capacity fade but a higher total resistance. A more stable SEI at the anode and greater surface modifications on the cathode are inferred to be the reasons for this non-intuitive behaviour.

Owning to extensively enhancement of renewable energy resources in the distribution grids, the employment of such sources is also associated with various issues such as the islanding problem. In this paper, an effective method has been proposed for detection of the islanding in the micro-grids comprised of inverter or direct fed types of distributed generations. The proposed method is designed based on the intrinsic modes of the voltage signal measured at the PCC point. More specifically, through the calculation of the positive sequence of the voltage signal variation (PSVSV) and extracting the signal energy of PSVSV's intrinsic modes, the islanding can be detected. The superiority of the proposed islanding detection method is manifested in the condition where the generation of distributed resources is in balance with the loading consumption. The performance of the proposed method has been evaluated considering the conditions where islanding is difficult to detect or might be mistaken with other phenomena given by loads within the NDZ region, different fault types, and loads with different power factors. The performance evaluation has been carried out through simulations, and furthermore has been compared with the state-of-the-art algorithms.

The inherent time-varying nature of the wind farm power causes undesired voltage flicker in the power network. In order to mitigate the flicker to enhance the performance of the wind power system with very fast dynamics, the static VAr compensator (SVC) is utilized. However, the SVC operates with some delays which negatively affects its performance. This persuades us to predict the reactive power of the wind farm to compensate for the real-world delay. The predicted reactive power is then utilized in the SVC. Therefore, this paper develops a novel fuzzy one-step-ahead prediction approach for the wind farm reactive power. The proposed fuzzy prediction uses a Takagi-Sugeno (TS) fuzzy representation whose unknown parameters are tuned online based on an extended Kalman filter (EKF). The wind farm is modeled as a time-varying current source which its amplitude and phase change every 0.01 s. A large set of the actual data of a wind farm in Manjil, Iran is gathered and directly utilized in the simulation process. Several flicker indices are calculated to evaluate the proposed prediction method. The obtained results show the performance enhancement and flicker mitigation of the suggested power scheme.

With a capacity-based network tariff structure, consumers are encouraged to reduce their connection capacity to avoid higher costs. However, overloading beyond the administrative grid connection capacity limit would result in an increased connection capacity, thus prosumers have to pay the increased electricity bill for the rest of the year. Therefore, it is important to optimize the energy generation and consumption profiles of local energy communities (LECs) considering the comfort level of occupants. This work aims to reduce the overloading of the grid connection and increase the utilization of local renewable energy resources (RES) thus avoids being penalized throughout the year due to casual intermittent overloading in peak hours, even once in a year. The present work proposes a novel data-driven flexibility optimizer model for day-ahead scheduling of energy profiles for LECs, considering photovoltaic (PV) generation, heat pump (HPs), and cooling loads. The proposed methodology has been developed to explore the flexibility potentials from a university campus network which includes both electrical and heating/cooling systems in an integrated way. A two-layer optimization strategy is developed, to guard the occupant’s comfort level. Simulation has been performed for complete two months, considering winter and summer scenarios. A peak demand reduction of 16% has been observed with negligible energy usage differences between the proposed and the baseline case. Two types of flexibility indicators are estimated to give a deeper insight into the performance. Economical gains of 9 % and 16 % are estimated depending on the type and voltage level of the connection.

In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO2-emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework.

The introduction of electric vehicles and photovoltaics is changing the residential electricity consumption. Distribution network operators (DNO) are investing in an advanced metering infrastructure (AMI) to enable cost reduction through smart grid applications. The DNO also benefits from the additional measurement data the AMI gives for the network planning process. The availability of AMI data can be limited by the cost of communication and by privacy concerns. To determine the social welfare of an AMI, the economic gains should be estimated. For the planning of the low voltage (LV) network, a method for determining the value of an AMI still needs to be developed. Therefore, a planning methodology which allows various levels of measurement data availability has been developed. By applying this approach the value of different levels of AMI from an LV-network planning perspective can be determined. To illustrated the application of this approach a case study for the LV-network of a Dutch DNO is performed. The results show that an increase in measurement data can lead to €49-254 lower LV-network reinforcement costs. A detailed analysis of the results shows that already 50% of the possible cost reduction can be achieved if only 65% of the households have AMI data available.

This paper deals with integration of energy storage systems into electricity markets. We explain why the energy storage systems increase flexibility of both power systems and energy markets and why such flexibility is desirable, particularly when variable renewable energy sources are being used in existing power systems. As opposed to the existing literature, our model includes a dual technology energy storage system, acting in two different markets. We introduce a mathematical formulation for this model applied to two Dutch electricity markets. Adopting optimal control approach with the goal to maximize the yearly benefit, we show that the dual energy storage system can be profitable already when the same buying/selling strategies are adopted for the working days and weekends. We show that the profitability (slightly) increases with different buying/selling strategies for the weekdays and weekends. Finally, we demonstrate how the yearly benefit varies with size and efficiency of the devices chosen and market prices.

Traditionally, extra high voltage (EHV) transmission systems consist exclusively of overhead lines (OHL) but in the recent years the utilization of high voltage AC (HVAC) power cables is increasing due to environmental, political and operational aspects. To study the impact of HVAC power cables on temporary overvoltages and slow front transients, a simulation model of the large-scale network is required. Guidelines concerning the modeling detail for the surrounding network need to be established to ensure sufficient accurate results. Existing guidelines for purely OHL transmission networks may not be adequate since the integration of HVAC power cables shift the harmonic impedance of the network to lower frequencies and that can affect the extent of the needed simulation model. Much effort can be saved from obtaining detailed data for an extensive but proper model, but a less detailed model might not describe the network behavior with sufficient accuracy. In this study, simplification options are considered, for which the time to construct and analyze a simulation model can be reduced. The simplification options are compared with the reference model (full detail), and judged based on three indicators for the level of accuracy: frequency and amplitude of the main resonance, and maximum over-voltage. As a result, guidelines on the extent of the simulated network and in which detail the model has to be built are proposed. The inverse approach, i.e. increasing level of detail until simulation results hardly change leads to similar results, but requires equivalent short-circuit networks for the rest of the grid. The method is tested for the Dutch 380 kV grid where an underground cable connection of 10.8 km length is introduced in 2013 and a second connection is planned for 2017.

Harmonic pollution is one of the most important power quality issues in electric power systems. Correct location of the main harmonic disturbance source is a key step to solve the problem. This paper presents a method to detect the location of harmonic disturbance source in low voltage network through Bayesian inference. The harmonic state is estimated based on the measurement data from limited measurement points whereby the measurement error is also considered. The performance of the proposed method is assessed through a case study applied in a typical low voltage network. Monte Carlo simulation is used to obtain the statistical results. The influence of different parameters like disturbance level, measurement accuracy level, etc. is discussed. The proposed method shows the adequacy for the analysis of distribution networks.

Electric load profiles are useful for accurate load forecasting, network planning and optimal generation capacity. They represent electricity demand patterns and are to a large extent predictable. However, new and heavier loads (heat pumps and electric vehicles), distributed generation, and home energy management technologies will change future energy consumption pattern of residential customers. This article analyses future residential load profiles via modelling and simulation of residential loads and distributed generations. The household base loads are represented by synthetic load profiles. Mathematical models are implemented for heat pumps, micro combined heat and power units, electric vehicles and photovoltaic systems. Scenario-based simulations are performed with different combination and penetration levels of load and generation technologies for different seasons. The results of the analyses show that with varying penetration levels of distributed generation and heavy loads, future residential load profiles will be more dynamic and dependent on multiple factors deviating from the classical demand pattern.

As part of its efforts to progress and innovate in the field of Smart Grid technologies, Enexis developed and currently operates a Battery Energy Storage System (BESS) called the Smart Storage Unit (SSU), part of the Smart Storage Project (SSP). The SSU is a 400 kVA, 232 kWh storage system, equipped with internet-connected control hardware in order to add ¿smartness¿ into the equation. The objective of the project is to enable field-testing and research on advanced electricity storage solutions in the LV distribution grid. This paper mainly focusses on the development of an addition to the controller system for the purpose of minimizing grid losses and maximizing grid asset utilization. Its principles rely on using historic data for load prediction, in order to flatten the power demand at the transformer, through peak-reduction and valley-filling.

Although it is well known that Fourier analysis is in reality only accurately applicable to steady state waveforms, it is a widely used tool to study and monitor time-varying signals, such as are commonplace in electrical power systems. The disadvantages of Fourier analysis, such as frequency spillover or problems due to sampling (data window) truncation can often be minimized by various windowing techniques, but they nevertheless exist. This paper demonstrates that it is possible to track and visualize amplitude and time-varying power systems harmonics, without frequency spillover caused by time–frequency techniques. This new tool allows for a clear visualization of time-varying harmonics, which can lead to better ways to track harmonic distortion and understand time-dependent power quality parameters. It also has the potential to assist with control, protection and power quality applications.

In electrical power systems there must always be a balance between supply and demand of power. Any imbalance will result in a frequency deviation. To reduce the imbalance to zero, ancillary services for balance management are in use. Ancillary services for balance management are characterized by their deployment time and their capacity. These ancillary services as well as the requirements for balance management can be characterized in the frequency domain. Actors, that are responsible for maintaining the balance in the system, can use the characterization in the frequency domain to define their needs for reserve capacity. This paper describes a method for spectral analysis of the required and available reserve capacity.

A triggered spark-gap switch together with a LCR trigger circuit has been developed in order to produce high levels of pulsed corona plasma at high-repetition rate and with a long lifetime. The spark-gap switch is flushed in air with a gas flow rate of up to 100 m 3 /h. Experiments are carried out up to 10 kW average output power, 12 J/pulse, and 900 pps (pulses per second).

The paper discusses the relationship between corona discharge modes and pollution emission control. It also discusses the optimization of corona plasma reactor and high-voltage pulse generator.

In this paper a distribution network is considered supplying electricity to a number of factories belonging to one industrial complex. The costs of interruption may vary more than one order of magnitude from one factory to another. The problem studied in this paper is how to adjust the time lags of the undervoltage protection relays in each factory in order to obtain a minimum in the total costs of interruptions of all factories together. By properly adjusting the time lags of the relays it may be provoked that factories with low interruption costs are disconnected from the network rather fast after the occurrence of a short circuit through which action the chance that a factory with high interruption cost is switched off is decreased.

Relative values of total collision cross sections are presented for Ar 2 , Ne 2 , and for a number of molecular dimers e.g. (N 2 ) 2 , with noble gases as scattering partner. The results follow a universal curve if plotted versus ν re1 /ϵ R m . From this plot the dimer van der Waals constant C 6 is derived being about twice as large as for the monomer, for the heavy systems.