Abstract This paper presents an overview of the development and perspectives of biogas in and its use for electricity, heat and in transport in the European Union (EU) and its Member States. Biogas production has increased in the EU, encouraged by the renewable energy policies, in addition to economic, environmental and climate benefits, to reach 18 billion m3 methane (654 PJ) in 2015, representing half of the global biogas production. The EU is the world leader in biogas electricity production, with more than 10 GW installed and a number of 17,400 biogas plants, in comparison to the global biogas capacity of 15 GW in 2015. In the EU, biogas delivered 127 TJ of heat and 61 TWh of electricity in 2015; about 50% of total biogas consumption in Europe was destined to heat generation. Europe is the world's leading producer of biomethane for the use as a vehicle fuel or for injection into the natural gas grid, with 459 plants in 2015 producing 1.2 billion m3 and 340 plants feeding into the gas grid, with a capacity of 1.5 million m3. About 697 biomethane filling stations ensured the use 160 million m3 of biomethane as a transport fuel in 2015.
Abstract The need for energy storage to balance intermittent and inflexible electricity supply with demand is driving interest in conversion of renewable electricity via electrolysis into a storable gas. But, high capital cost and uncertainty regarding future cost and performance improvements are barriers to investment in water electrolysis. Expert elicitations can support decision-making when data are sparse and their future development uncertain. Therefore, this study presents expert views on future capital cost, lifetime and efficiency for three electrolysis technologies: alkaline (AEC), proton exchange membrane (PEMEC) and solid oxide electrolysis cell (SOEC). Experts estimate that increased R&D funding can reduce capital costs by 0–24%, while production scale-up alone has an impact of 17–30%. System lifetimes may converge at around 60,000–90,000 h and efficiency improvements will be negligible. In addition to innovations on the cell-level, experts highlight improved production methods to automate manufacturing and produce higher quality components. Research into SOECs with lower electrode polarisation resistance or zero-gap AECs could undermine the projected dominance of PEMEC systems. This study thereby reduces barriers to investment in water electrolysis and shows how expert elicitations can help guide near-term investment, policy and research efforts to support the development of electrolysis for low-carbon energy systems.
Superior electricity-optimized business ecosystems (EOBEs) have evolved into pivotal determinants in catalyzing industrial–commercial prosperity. The access to electricity index (AEI) constitutes a valid instrument for assessing the EOBE. To realize the accurate evaluation of EOBE and the root cause tracing of its changes, this paper constructs a new analytical model for evaluating and monitoring changes in EOBE. First, this paper constructs a new evaluation model of EOBE based on the Business Ready (B-READY) evaluation system, considering three factors: the power regulatory quality, the public service level, and the enterprises’ gain power efficiency. Then, the model uses the raw data collected to calculate a score for AEI to enable an accurate assessment of EOBE. Next, this paper uses a priori assessment to extract the coupling features of indicators and combines the time series features and policy features to construct the feature matrix. Finally, the characteristic contribution was analyzed using support vector regression (SVR) and Shapley’s additive interpretation (SHAP) value. The experiment shows that the factors affecting the change in AEI are time series features, policy features, and coupling features in decreasing order of importance. This study provides reference cases and improvement ideas for the assessment and optimization of EOBE.
This review article is the third of a three-article introductory series on virtual cathode oscillators. The first article has laid the theoretical ground for understanding the physical properties of the virtual cathode, and the second article has provided a numerical tool for studying virtual cathode oscillation. This third article focuses on the interaction between the electron beam and electromagnetic field. The virtual cathode oscillator has been studied for decades with the aim of developing it as high-power microwave source. The beam-field interaction has been one of the core issues that always perplexes both experimentalists and theorists. Using the physical model established in the first article and the numerical method described in the second article, this article is an attempt to answer some of the key questions based on a more comprehensive description of the device and its interaction process. This article is expected to serve as a reference for young researchers and students working on high-power microwaves and pulsed particle beams.
With stakeholder-level in-market data, we conduct a comparative analysis of machine learning (ML) for forecasting electricity prices in Singapore, spanning 15 individual models and 4 ensemble approaches. Our empirical findings justify the three virtues of ML models: (1) the virtue of capturing non-linearity, (2) the complexity (Kelly et al., 2024) and (3) the l2-norm and bagging techniques in a weak factor environment (Shen and Xiu, 2024). Simulation also supports the first virtue. Penalizing prediction correlation improves ensemble performance when individual models are highly correlated. The predictability can be translated into sizable economic gains under the mean-variance framework. We also reveal significant patterns of time-series heterogeneous predictability across macro regimes: predictability is clustered in expansion, volatile market and extreme geopolitical risk periods. Our feature importance results agree with the complex dynamics of Singapore's electricity market after de regulation, yet highlight its relatively supply-driven nature with the continued presence of strong regulatory influences.
Since the 1990s, widespread introduction of central (wholesale) electricity markets has been seen across multiple continents, driven by the search for efficient operation of the power grid through competition. The increase of renewables has made significant impacts both on central electricity markets and distribution-level grids as renewable power generation is often connected to the latter. These stochastic renewable technologies have both advantages and disadvantages. On one hand they offer very low marginal cost and carbon emissions, while on the other hand, their output is uncertain, requiring flexible backup power with high marginal cost. Flexibility from end-prosumers or smaller market participants is therefore seen as a key enabler of large-scale integration of renewables. However, current central electricity markets do not directly include uncertainty into the market clearing and do not account for physical constraints of distribution grids. In this paper we propose a local electricity market framework based on probabilistic locational marginal pricing, effectively accounting for uncertainties in production, consumption and grid variables. The model includes a representation of the grid using the lindistflow equations and accounts for the propagation of uncertainty using general Polynomial Chaos (gPC). A two-stage convex model is proposed; in the day-ahead stage, probability distributions of prices are calculated for every timestep, where the expected values represent the day-ahead (spot) prices. In the real-time stage, uncertainties are realized (measured) and a trivial calculation reveals the real-time price. Through four instructive case-studies we highlight the effectiveness of the method to incentivize end-prosumers' participation in the market, while ensuring that their behavior does not have an adverse impact on the operation of the grid.
Electricity storage is used for intertemporal price arbitrage and for ancillary services that balance unforeseen supply and demand fluctuations via frequency regulation. We present an optimization model that computes bids for both arbitrage and frequency regulation and ensures that storage operators can honor their market commitments at all times for all fluctuation signals in an uncertainty set inspired by market rules. This requirement, initially expressed by an infinite number of nonconvex functional constraints, is shown to be equivalent to a finite number of deterministic constraints. The resulting formulation is a mixed-integer bilinear program that admits mixed-integer linear relaxations and restrictions. Empirical tests on European electricity markets show a negligible optimality gap between the relaxation and the restriction. The model can account for intraday trading and, with a solution time of under 5 seconds, may serve as a building block for more complex trading strategies. Such strategies become necessary as battery capacity exceeds the demand for ancillary services. In a backtest from 1 July 2020 through 30 June 2024 joint market participation more than doubles profits and almost halves energy storage output compared to arbitrage alone.
Electric vehicle charging and geo-distributed datacenters introduce spatially flexible loads (FLs) that couple power, transportation, and datacenter networks. These couplings create a closed-loop feedback between locational marginal prices (LMPs) and decisions of the FL systems, challenging the foundations of conventional competitive equilibrium (CE) in electricity markets. This paper studies a notion of generalized competitive equilibrium (GCE) that aims to capture such price-demand interactions across the interconnected infrastructures. We establish structural conditions under which the GCE preserves key properties of the conventional CE, including existence, uniqueness, and efficiency, without requiring detailed knowledge of decision processes for individual FL systems. The framework generalizes to settings where the grid is coupled with multiple FL systems. Stylized examples and case studies on the New York ISO grid, coupled with the Sioux Falls transportation and distributed datacenter networks, demonstrate the use of our theoretical framework and illustrate the mutual influence among the grid and the studied FL systems.
Somil Yadav, Caroline-Hachem Vermette, Md.Nadim Heyat Jilani
et al.
Double Skin Façade (DSF) system comprises two glazing layers with a ventilated cavity. Integrating photovoltaic (PV) modules within the outer layer of DSFs offers an efficient method for electricity generation. Current tools for modeling and analyzing DSF systems are complex and resource-intensive, lacking the capability to evaluate the performance of innovative PV-DSF systems during the early design stage. This study develops a mathematical model to evaluate the electrical and thermal performance of PV-DSF systems, considering architectural design elements such as PV color and relative orientation. Based on an energy balance approach, the model is particularly suited for designing PV-DSF systems in heritage buildings, which often have color and relative orientation constraints. The model is applied to assess the performance of PV-DSF systems with conventional clear glass PV and colored front glass PV modules under the climatic conditions of Montreal, Canada. Results indicated that conventional clear glass PV module exhibit higher PV cell temperature than colored PV modules due to greater transmissivity, with peak temperature differences at noon of 5.5 °C, 6.2 °C, and 6.5 °C for orange, blue, and gray PV modules, respectively. On the contrary, the influence of PV's color front glass on room air temperature is non-significant. Furthermore, the optimal orientation for maximum energy yield is not always south-facing; it depends on the hourly distribution of the beam, diffuse solar irradiation, and ambient air temperature. For Montreal, west-facing DSFs produce more electrical and thermal energy on a summer design day because the hourly distribution of beam radiation is skewed towards afternoon hours.
Marlon Mesquita Lopes Cabreira, Felipe Leite Coelho da Silva, Josiane da Silva Cordeiro
et al.
The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.
Abstract Implementation of reconfigurable metasurfaces demands a complex structure. The design methodology for metasurface elements based on PIN diodes requires targeted optimisation tailored to the varying application environments and cannot be directly transplanted from one methodological approach to another. In addition, inclusion of varactors often imposes undesirable high‐power consumption with complex analog drive circuitry. Furthermore, the design method for the reconfigurable metasurface element has not been simplified and unified, hindering the rapid deployment of metasurface applications. The authors propose a general design model based on low‐power embedded circuitry for a multi‐function metasurface element and give the corresponding numerical analysis. The authors present a reconfigurable method based on the general design model and prove that the method achieves independent regulation of amplitude and phase for the reflected electromagnetic wave. Through analysis of the sensitive area at the bottom of the embedded circuit, a gradient descent algorithm is proposed to find the optimised phase points according to device sensitivity of the application scenario and suitable strategy. Finally, the authors demonstrate that the designed metasurface element possesses phase control capability and decent phase consistency obtained via far‐field scanning experiments. The proposed element design method will provide a theoretical reference for the integrated design of metasurface.
Arega Getaneh Abate, Dorsa Majdi, Jalal Kazempour
et al.
It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perfectly or with some uncertainty. This is not necessarily a realistic assumption. This paper takes a different approach by relaxing such an assumption and exploits a no-regret learning algorithm for repeated games. In particular, by using the \emph{a posteriori} information about rivals' bids, a learner can implement a no-regret algorithm to optimize her/his decision making. Given this information, we utilize a multiplicative weight-update algorithm, adapting bidding strategies over multiple rounds of an auction to minimize her/his regret. Our numerical results show that when the proposed learning approach is used the social cost and the market-clearing prices can be higher than those corresponding to the classical game-theoretic approaches. The takeaway for market regulators is that electricity markets might be exposed to greater market power of suppliers than what classical analysis shows.
Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes
et al.
Electric power systems are undergoing a major transformation as they integrate intermittent renewable energy sources, and batteries to smooth out variations in renewable energy production. As privately-owned batteries grow from their role as marginal "price-takers" to significant players in the market, a natural question arises: How do batteries operate in electricity markets, and how does the strategic behavior of decentralized batteries distort decisions compared to centralized batteries? We propose an analytically tractable model that captures salient features of the highly complex electricity market. We derive in closed form the resulting battery behavior and generation cost in three operating regimes: (i) no battery, (ii) centralized battery, and (ii) decentralized profit-maximizing battery. We establish that a decentralized battery distorts its discharge decisions in three ways. First, there is quantity withholding, i.e., discharging less than centrally optimal. Second, there is a shift in participation from day-ahead to real-time, i.e., postponing some of its discharge from day-ahead to real-time. Third, there is reduction in real-time responsiveness, or discharging less in response to smoothing real-time demand than centrally optimal. We also quantify the impact of the battery market power on total system cost via the Price of Anarchy metric, and prove that the it is always between $9/8$ and $4/3$. That is, incentive misalignment always exists, but it is bounded even in the worst case. We calibrate our model to real data from Los Angeles and Houston. Lastly, we show that competition is very effective at reducing distortions, but many market power mitigation mechanisms backfire, and lead to higher total cost.