Search

Increasing the accuracy and intelligence of short-term load forecasting system can improve modern power systems management and economic power generation. In recent decades, the optimized machine learning methods have been widely used in load forecasting problems because of their predictability with higher accuracy and robustness. However, most related researches only use evolutionary algorithms for parameters fine-tuning and ignore the evolutionary algorithm based decision-making support and the matching relation between the used evolutionary algorithm and machine learning method, which greatly limit the improvement of forecasting system. To dissolve the above issues, a data-driven evolutionary ensemble learning forecasting model is proposed in this paper. Firstly, a novel multimodal evolutionary algorithm based on comprehensive weighted vector angle and shift-based density estimation is proposed. Secondly, based on the proposed multimodal evolutionary algorithm, an intelligent decision-making support scheme including predictive performance evaluation, model properties analysis, structure and fusion strategy optimization, and optimal model preference selection is designed to improve the random vector functional link network based ensemble learning model and boost the forecasting accuracy. Thirdly, experimental studies on 15 test problems with up to 6000 decision variables are conducted to validate the excellent optimization ability of the proposed evolutionary algorithm. Finally, the proposed evolutionary ensemble learning method is compared with 6 other representative forecast methods on real-world short-term load forecasting datasets from Australia, Great Britain, and Norway. The experiment results verify the superiority and applicability of the proposed method. • A new multimodal evolutionary algorithm (MMOWVA) is being proposed. • MMOWVA is validated by comparison experiments on 15 multimodal problems. • An expandable intelligent scheme is offered to make decision-makers convenient. • An evolutionary ensemble learning model is designed to improve forecast accuracy. • Experiment results demonstrate a higher forecast accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The electricity market in the UK has been reformed over recent years resulting in the introduction of 'Contracts for Difference' as an option for wind producers. They can use these contracts only if at the same time they enter into other market contracts, i.e., Power Purchase Agreements or participation in the wholesale market. Most wind producers prefer the option of holding a long-term Power Purchase Agreement with another party. However, these agreements do not always offer the best terms for a wind producer due to them selling energy at lower electricity prices than those of the wholesale market spot prices. In addition, direct participation in the wholesale market can lead to high imbalance costs for the wind producers. In this paper, we propose an optimal dispatch model solved via dynamic programming optimisation to examine these two different business cases for wind operators alongside the existence (or not) of subsidised Contracts for Difference. A case study with an onshore UK wind farm and a lithium-based battery is provided. The proposed methodology was designed flexibly, so that it can be applied to different wind farm and battery configurations, as well as energy market structures. It is found that it is not always more profitable for the wind operator to hold a Power Purchase Agreement with another party than owning a battery storage unit and trading wind energy directly into the wholesale market. The discount rate of the Power Purchase Agreements, the capital cost of the wind farm and the strike price of the Contracts for Difference are critical factors affecting the profitability of both business cases. In addition, if subsidies are totally removed from both of them, then these are no longer economically justifiable, unless significant reductions are achieved in the capital costs of wind farms and battery storage units. Lastly, we show that considering only the calendar life of the battery (and not the cycle life) leads to significant underestimation of the investment costs. • Dynamic programming proposed for optimally valuing battery storage for wind farms. • Annual revenue of wind farm increased with the battery storage unit. • Bilateral contracts are not always more profitable than trading in wholesale market. • Without subsidies, the investment in wind battery storage is not profitable. • Neglecting battery's cycling life leads to undervaluation of the investment. [ABSTRACT FROM AUTHOR]

Although the penetration of renewable energy in power systems has been substantially increased globally in the last decade, fossil fuels are still important in providing the essential flexibility required to reliably maintain the system balance. In 2019, more than one quarter of power generation in Europe and over 40% of the UK's electricity generation was from fossil fuels (mainly gas). For achieving the net-zero greenhouse gas emission target around the middle of this century, these fossil fuels have to be decarbonised in the coming decades. Bulk-scale energy storage has been recognised as a key technology to overcome the reduced dispatchability associated with the decrease of fossil fuels in generation. Taking the UK power system as a case study, this paper presents an assessment of geological resources for bulk-scale compressed air energy storage (CAES), and an optimal planning framework for CAES in combination with solar and wind to replace fossil fuels in the power generation system. The analysis reveals up to 725 GWh of ready-to-use capacity by utilising existing underground salt caverns in the UK. These potential CAES sites with added solar and wind generation equal to the generation from fossil fuels in 2018 can reduce carbon emissions by 84% with a cost increase by 29%, compared to the system in 2018. The results indicate the plausibly achievable cost-effectiveness of CAES as bulk-scale energy storage for power system decarbonisation in countries the geological resources are available. [ABSTRACT FROM AUTHOR]

• The economic and environmental feasibility of peer-to-peer trading are analysed. • Novel algorithms and techniques to determine feasibility are presented. • Results suggest Peer-to-peer trading could become economically feasible within 10 years. • Reconductoring is more economically feasible the batteries & curtailment as a constraint management strategy. • The same observation is made when analysed from an environmental perspective. With the recent removal of the solar PV feed-in tariff on ≤4 kWp arrays, installation of domestic rooftop PV has become significantly less commercially feasible in the UK. Whilst alternative trading schemes are beginning to emerge, the adequacy of their return on investment is questionable. However, the extent to which installation may need to be subsidised has not yet been quantified. Furthermore, the extent to which increased peer-to-peer trading could contribute to network constraints, and the environmental and economic costs of managing such schemes, have not been examined extensively. In this paper, the subsidy required to encourage a rooftop PV uptake rate of 0.5% homeowners/year is determined and the costs of constraint management are calculated for various network topologies. The problems presented are solved using time series analysis, power flow simulations, and optimisation processes with Monte Carlo methods. It was found that P2P trading is the most promising post subsidy revenue stream, reducing the required feed-in tariff from £0.14 to £0.10 for systems installed in 2020. Furthermore, it was found that with P2P trading, requirement for feed-in tariff support should end by 2032. Reconductoring was the most economically effective constraint management strategy, costing £50 k to £200 k less than curtailment when applied to 29 networks over 30 years. From an environmental perspective, it was found that reconductoring had a carbon footprint 2 orders of magnitude lower than curtailment & battery storage. [ABSTRACT FROM AUTHOR]

• A mechanism was designed for ancillary service provision from P2P energy trading. • A continuous double auction was adopted as the P2P energy trading mechanism. • A residual balancing mechanism was designed for addressing the unbalance. • Optimal bidding strategies of customers in the proposed mechanisms were proposed. • The proposed mechanisms are beneficial for individuals, community and power grid. As an innovative paradigm for electric power systems with a high penetration of distributed energy resources, peer-to-peer (P2P) energy trading enables direct energy trading between end customers, which is able to facilitate local power and energy balance and potentially support the operation of bulk power systems. In this paper, a framework was proposed to enable ancillary service provision from a P2P energy trading community, creating additional value for both customers in the community and power systems. Specifically, an ancillary service provision mechanism was designed along with P2P energy trading and residual balancing mechanisms to enable the power utility to obtain ancillary service from customers in a P2P energy trading community. Furthermore, the optimal bidding strategy of customers was figured out to maximize their benefits in the proposed mechanisms. Simulation studies were conducted based on a residential community in Great Britain. The results show that the proposed ancillary service mechanism can enable the power utility to obtain a significant or required amount of ancillary services of different types. The proposed mechanisms and optimal bidding strategy can achieve Pareto improvement for the revenue of each customer and result in significantly higher social welfare for the whole community. It is also revealed that increasing ancillary service prices and installation rate of electric vehicles can increase the total amount of ancillary service provision and thus bring higher revenue for the customers in the community. By contrast, increasing installation of PV systems does not necessarily increase the amount of service provision. [ABSTRACT FROM AUTHOR]

• An off-design model for liquid air energy storage (LAES) is developed. • Off-design conditions across LAES components lead to inefficiencies. • Suitable regulation is proven to mitigate the extent of part-load inefficiencies. • LAES performance can vary by up to 30%, depending on the market services provided. • Link between LAES techno-economic value and its operating mode is elucidated. This paper deals with Liquid Air Energy Storage (LAES) – one of the most promising thermo-mechanical technologies with the potential to provide bulk energy storage functionalities. More specifically, an integrated technical and economic assessment of the performance of LAES when providing multiple energy and frequency control ancillary services is carried out. To this end, an off-design thermodynamic model of LAES was developed and validated against experimental data from the literature to allow understanding the links between the specific requirements of the market services to provide and the performance of each component of the LAES plant, and from here the performance of whole LAES process. The model was then applied to assess how a stand-alone LAES plant performs when providing three specific assets in the UK electricity market: arbitrage, short term operating reserve (STOR), and fast reserve (FR). The results obtained clearly demonstrate that (a) a strong link between type of service and LAES off-design conditions exists and cannot be neglected; (b) roundtrip efficiency and liquid air consumption can vary by up to 30% during off-design operation, causing some 10 k£/MW of missed revenue; (c) the effect of off-design conditions is unevenly distributed across LAES components, with low-pressure turbines affected the most; (d) a suitable regulation strategy can alleviate but not prevent off-design performance variations, improving roundtrip efficiency by up to 4 percentage points. The modelling developed is thus essential for a realistic assessment of the value and constraints for LAES to participate in electricity markets and support low-carbon power system operation and development. [ABSTRACT FROM AUTHOR]

• Extends the classic NILM research from household level to substation level. • Deduces the states of different types of loads in a region including PV and EV. • The proposed method improves classic sparse coding, OMP and NMP. • Robust with higher DERs penetration rates and larger forecasting errors. This paper presents a novel extension of the classic nonintrusive load monitoring (NILM) problem from household-appliance level to substation level. A new three-stage regional-NILM method is proposed to deduce the states of different types of loads in a region by disaggregating its substation demand. Three types of loads are considered in this study: (i) traditional loads; (ii) distributed generation such as photovoltaics (PVs); and (iii) flexible loads like electric vehicles (EVs). The proposed method firstly forecasts the traditional load using the long-term historical data and employing spectral analysis to boost the signal-to-noise ratio. Secondly, the PV capacity is deduced by performing peak coincidence analysis between negative residuals and local solar irradiance data. Finally, a novel limited activation matching pursuit method is proposed to estimate the states of the EVs, including the total EV load and number of EVs. The method is assessed on real data collected from 800 substations, 10 PVs and 50 EVs in the UK. Results show the proposed method for estimating the number of EVs outperforms the approaches based on sparse coding, orthogonal matching pursuit and non-negative matching pursuit by 16.5%, 10.2% and 10.0%, respectively. The proposed Regional-NILM solution provides a cost-effective way for distribution network operators to understand the network's state. It can therefore significantly increase the network visibility without requiring expensive monitoring and avoiding data privacy issues. As such, it can improve the efficiency of demand side management, which is required to accommodate the future large number of distributed energy resources connections. [ABSTRACT FROM AUTHOR]

• V2G can reduce peak demand up to 40% further than unidirectional smart charging. • Both charging losses and resistive network losses increase as a result. • The benefit of V2G is largest when around 20% of households on a network have an EV. • Solar generation on the network significantly increases its value. In recent years, there has been growing interest in AC vehicle-to-grid charging in residential networks. Bi-directional smart charging offers additional flexibility compared to uni-directional smart chargers, but are expensive to install and implement. This paper investigates the costs and benefits to the distribution network that bi-directional charging provides, relative to uni-directional charging. Benefit is quantified in terms of the reduction in the peak demand of the network, and costs are quantified with the increase in throughput of the vehicles' batteries and the increase in electrical losses in the system. Measured data and representative networks are used to construct two representative case studies of residential charging, one in the UK and one in Texas, US. [ABSTRACT FROM AUTHOR]

• The inconvenience of charging electric vehicles (EVs) is quantified by its time penalty. • Idealised charging schedules are derived from over 39,000 week-long travel diaries. • Convenience parity occurs when EV charging carries the same time penalty as petrol car fuelling. • Drivers who cannot charge at home are expected to suffer significant inconvenience. • Very few long journeys are delayed due to charging if natural breaks are taken as recommended. This paper presents a quantitative investigation of the inconvenience of electric vehicle (EV) charging relative to internal combustion engine vehicle (ICEV) fuelling in terms of the time penalty likely to be experienced by drivers. A heuristic approach to deriving idealised charging schedules from over 39,000 week-long travel diaries from the UK National Travel Survey is presented in order to quantify the expected convenience parity — the point at which EV charging and ICEV fuelling are of comparable convenience — for combinations of battery capacity, charger power and access to charging at different locations (home, workplace and public destinations). It was found that although the majority — up to 95% — of individuals who can charge at home are expected to be able to reach convenience parity with battery sizes currently available in EV models at the 'affordable' end of the market, this is significantly less likely for those who rely on workplace or public charging — and particularly for those who must rely solely on en route charging. These individuals are expected to suffer considerable inconvenience associated with EV charging relative to ICEV fuelling, and although greater battery capacities and charger power ratings are expected to lessen this inconvenience, there remains a significant gap in the convenience of EV ownership between those who can charge while parked at home and those who cannot. Further analysis is carried out to long journeys that cannot be made on a single charge; 'range anxiety' being a major obstacle to widespread EV adoption. It was found that if drivers are compliant with the UK Highway Code in taking regular breaks on long journeys, fewer than 0.01% of trips are expected to be delayed by charging when using battery capacities of 40–60 kWh. [ABSTRACT FROM AUTHOR]

• Tank size and temperature set point of 1.2 m3 and 75 °C are optimal system design. • Should charge storage at 3am and 2pm for morning and afternoon demands, respectively. • The best load shifting positively affects the grid. • Achieve running costs and CO 2 savings with the best load shifting, compared to oil boilers. Cascade air-to-water heat pumps may have good potential for retrofitting UK domestic buildings because they can directly replace existing fossil-fuel boilers without the requirement of considerable modifications to heat distribution systems. A widespread uptake of these heat pumps, however, would pose challenges to the grid. Furthermore, wind power generation has increased in the UK to achieve the target of decreasing CO 2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. In this paper, a load shifting study for cascade heat pumps coupled with thermal energy storage addressing these issues is presented. The main objective is to find the best tariff-based schedule load shifting for cascade heat pumps, which can help to avoid peak demand periods while obtaining enhanced system energy efficiency with minimised running costs and reduced wind energy curtailment. How the retrofit performance of the cascade heat pumps with load shifting is further investigated. TRNSYS was used to simulate the system performance validated against experimental results. Northern Ireland (UK) was selected as the evaluated scenario. Simulation results showed that the tank temperature set point of 75 °C and the storage size of 1.2 m 3 could wholly shift the cascade heat pumps' operation to off-peak periods. The best times to start the cascade heat pumps to charge the storage were at 3 am and 2 pm for the morning and afternoon heating demands, respectively. Compared to oil boilers, the cascade heat pumps with load shifting could obtain lower running costs (16–34%) and carbon emissions (20–37%). [ABSTRACT FROM AUTHOR]

• Novel subsampling approach for timeseries inputs in power system models is introduced. • Allows use of multiple decades of demand & weather data in power system planning. • Method's performance is superior to established timeseries reduction approaches. • Approach applicable to wide class of optimisation-based power system planning models. Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling , allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation-based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios. [ABSTRACT FROM AUTHOR]

• An accelerated EV program is needed to meet 2050 CO2 emission targets for the UK. • Even under accelerated uptake, few CO 2 benefits will be seen before 2030. • The lack of impact before 2030 derives from slow vehicle stock turnover. • With embedded production CO2, 2050 UK targets will need intense grid decarbonisation. • There is an urgent need to pursue both EV uptake and demand side solutions. The decarbonisation of the road transport sector is increasingly seen as a necessary component to meet global and national targets as specified in the Paris Agreement. It may be achieved best by shifting from Internal Combustion Engine (ICE) cars to Electric Vehicles (EVs). However, the transition to a low carbon mode of transport will not be instantaneous and any policy or technological change implemented now will take years to have the desired effect. Within this paper we show how on-road emission factors of EVs and models of embedded CO 2 in the vehicle production may be combined with statistics for vehicle uptake/replacement to forecast future transport emissions. We demonstrate that EVs, when compared to an efficient ICE, provide few benefits in terms of CO 2 mitigation until 2030. However, between 2030 and 2050, predicted CO 2 savings under the different EV uptake and decarbonisation scenarios begin to diverge with larger CO 2 savings seen for the accelerated EV uptake. This work shows that simply focusing on on-road emissions is insufficient to model the future CO 2 impact of transport. Instead a more complete production calculation must be combined with an EV uptake model. Using this extended model, our scenarios show how the lack of difference between a Business as Usual and accelerated EV uptake scenario can be explained by the time-lag in cause and effect between policy changes and the desired change in the vehicle fleet. Our work reveals that current UK policy is unlikely to achieve the desired reduction in transport-based CO 2 by 2030. If embedded CO 2 is included as part of the transport emissions sector, then all possible UK EV scenarios will miss the reduction target for 2050 unless this is combined with intense decarbonisation (80% of 1990 levels) of the UK electricity grid. This result highlights that whilst EVs offer an important contribution to decarbonisation in the transport sector it will be necessary to look at other transport mitigation strategies, such as modal shift to public transit, car sharing and demand management, to achieve both near-term and long-term mitigation targets. [ABSTRACT FROM AUTHOR]