• Energy Research
• Open Access close
• US close
• Energy close

In the current situation with the unprecedented deployment of clean technologies for electricity generation, it is natural to expect that storage will play an important role in electricity networks. This paper provides a qualitative methodology to select the appropriate technology or mix of technologies for different applications. The multiple comparisons according to different characteristics distinguish this paper from others about energy storage systems. Firstly, the different technologies available for energy storage, as discussed in the literature, are described and compared. The characteristics of the technologies are explained, including their current availability. In order to gain a better perspective, availability is cross-compared with maturity level. Moreover, information such as ratings, energy density, durability and costs is provided in table and graphic format for a straightforward comparison. Additionally, the different electric grid applications of energy storage technologies are described and categorised. For each of the categories, we describe the available technologies, both mature and potential. Finally, methods for connecting storage technologies are discussed.

Abstract This paper uses quantile regression to demonstrate how electricity price distributions are linked to fundamental supply and demand variables. It investigates the California electricity market (zone SP15) for selected trading hours using data from January 8, 2013 to September 24, 2016. The approach quantifies a non-linear relationship between the fundamentals and electricity prices, just as predicted by the merit order curve. Natural gas, greenhouse gas allowance prices and load all have a positive effect on electricity prices, with the effect increasing with the quantiles. In contrast, solar production and wind production both have a negative effect on electricity prices. The effect of solar production increases with quantiles, whereas the effect of wind production decreases with quantiles. This paper also includes a stress testing case study in which a producer faces the risk of high solar and wind production, and investigates the effect on the lower tail of the price distribution. Overall, the results demonstrate how the proposed approach can be a helpful risk management tool for participants in the electricity market.

Abstract The use of compressed air in industry is an important and yet overlooked energy carrier. Although there are different energy-saving measures discussed in the specialized literature, there is little discussion on the energy performance of the production and use of compressed air. This study developed a new approach to assess the energy performance of compressed air systems based on a six-step local energy benchmarking methodology. The methodology includes an energy management procedure to monitor and control the electricity consumption and sustain the energy performance of compressed air systems in time. The procedure monitors the production and use of compressed at plant and at manufacturing section levels based on the real-time monitoring of relevant variables to calculate energy performance indicators, energy baselines, and CUSUM charts. Monitoring the consumption of compressed air at the section level in a case study reduced the demand between 11 and 47%. While electricity consumption to produce compressed air at the plant level reduced by an estimated 23%. This approach permits the rapid detection of inefficiencies in the production and demand sides of the compressed air system, highlighting inefficiencies that are frequently hidden in the total electricity consumption of manufacturing plants.

Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.

Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

Abstract Ammonia–water mixtures have been used as working fluids in absorption–refrigeration cycles for several decades. Their use as multi-component working fluids for power cycles has been investigated recently. The thermodynamic properties required are known or may be calculated at elevated temperatures and pressures. We present a new method for these computations using Gibbs free energies and empirical equations for bubble and dew point temperature to calculate phase equilibria. Comparisons of calculated and experimental data show excellent agreement.

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation toward more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix–based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analysing future scenarios, including electricity participation in end–use consumption and electric power generated by renewables —potentially integrated into different sectors—. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix–based model can be also applied to other areas and energy resources, as an alternative way to analyse and estimate renewable integration into different sectors. 2023-24

Abstract Shifts among economic sectors within manufacturing have had significant effects on industrial energy demand since 1973. At least one-third of the reduction in U.S. energy intensity for fossil fuels can be attributed to sectoral shifts over this historical period. Shifts among economic sectors will continue to be an important source of uncertainty in forecasting industrial energy demand. Without greater consensus on the major causes of these shifts, analysts will be unable to separate how much of the past shifts can be reversed with changed energy and economic conditions and how much will remain embedded in the economic structure. Standard economic projections anticipate a continuation of the shift away from large industrial energy-using sectors, although at a slower rate. The Wharton economic projections used recently in an Energy Modeling Forum (EMF) study indicated a decline rate of about half that experienced during the 1973–1981 period. Even so, this effect alone could contribute as much as 0.5% per annum to the rate of decline in aggregate energy intensity within manufacturing.