Results 1 - 10 of 19
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[en] Blind faith is unlikely to produce a free market that is competitive. Substituting markets for traditional regulation is only one choice among many policy instruments to achieve a goal of lower prices; such substitution should not be in itself a goal. (author)
[en] We examine the cost of carbon dioxide mitigation to consumers in restructured USA markets under two policy instruments, a carbon price and a renewable portfolio standard (RPS). To estimate the effect of policies on market clearing prices, we constructed hourly economic dispatch models of the generators in PJM and in ERCOT. We find that the cost effectiveness of policies for consumers is strongly dependent on the price of natural gas and on the characteristics of the generators in the dispatch stack. If gas prices are low (∼$4/MMBTU), a technology-agnostic, rational consumer seeking to minimize costs would prefer a carbon price over an RPS in both regions. Expensive gas (∼$7/MMBTU) requires a high carbon price to induce fuel switching and this leads to wealth transfers from consumers to low carbon producers. The RPS may be more cost effective for consumers because the added energy supply lowers market clearing prices and reduces CO_2 emissions. We find that both policies have consequences in capacity markets and that the RPS can be more cost effective than a carbon price under certain circumstances: continued excess supply of capacity, retention of nuclear generators, and high natural gas prices. (letter)
[en] Low natural gas prices, market reports and evidence from New York State suggest that the number of commercial combined heat and power (CHP) installations in the United States will increase by 2%–9% annually over the next decade. We investigate how increasing commercial CHP penetrations may affect net emissions, the distribution network, and total system energy costs. We constructed an integrated planning and operations model that maximizes owner profit through sizing and operation of CHP on a realistic distribution feeder in New York. We find that a greater penetration of CHP reduces both total system energy costs and network congestion. Commercial buildings often have low and inconsistent heat loads, which can cause low fuel utilization efficiencies, low CHP rates-of-return and diminishing avoided emissions as CHP penetration increases. In the northeast, without policy intervention, a 5% penetration of small commercially owned CHP would increase CO2 emissions by 2% relative to the bulk power grid. Low emission CHP installations can be encouraged with incentives that promote CHP operation only during times of high heat loads. Time-varying rates, such as time-of-day and seasonal rates, are one option and were shown to reduce customer emissions without reducing profits. In contrast, natural gas rate discounts, a common incentive for industrial CHP in some states, can encourage CHP operation during low heat loads and thus increase emissions. (letter)
[en] We examine the potential for geographic smoothing of solar photovoltaic (PV) electricity generation using 13 months of observed power production from utility-scale plants in Gujarat, India. To our knowledge, this is the first published analysis of geographic smoothing of solar PV using actual generation data at high time resolution from utility-scale solar PV plants. We use geographic correlation and Fourier transform estimates of the power spectral density (PSD) to characterize the observed variability of operating solar PV plants as a function of time scale. Most plants show a spectrum that is linear in the log–log domain at high frequencies f, ranging from to (slopes of −1.23 and −1.56), thus exhibiting more relative variability at high frequencies than exhibited by wind plants. PSDs for large PV plants have a steeper slope than those for small plants, hence more smoothing at short time scales. Interconnecting 20 Gujarat plants yields a spectrum, reducing fluctuations at frequencies corresponding to 6 h and 1 h by 23% and 45%, respectively. Half of this smoothing can be obtained through connecting 4–5 plants; reaching marginal improvement of 1% per added plant occurs at 12–14 plants. The largest plant (322 MW) showed an spectrum. This suggests that in Gujarat the potential for smoothing is limited to that obtained by one large plant. (letter)
[en] We use time- and frequency-domain techniques to quantify the extent to which long-distance interconnection of wind plants in the United States would reduce the variability of wind power output. Previous work has shown that interconnection of just a few wind plants across moderate distances could greatly reduce the ratio of fast- to slow-ramping generators in the balancing portfolio. We find that interconnection of aggregate regional wind plants would not reduce this ratio further but would reduce variability at all frequencies examined. Further, interconnection of just a few wind plants reduces the average hourly change in power output, but interconnection across regions provides little further reduction. Interconnection also reduces the magnitude of low-probability step changes and doubles firm power output (capacity available at least 92% of the time) compared with a single region. First-order analysis indicates that balancing wind and providing firm power with local natural gas turbines would be more cost-effective than with transmission interconnection. For net load, increased wind capacity would require more balancing resources but in the same proportions by frequency as currently, justifying the practice of treating wind as negative load. (letter)
[en] Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16–0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07–0.13 MW of dispatchable capacity for each MW of additional wind capacity. (letter)
[en] We investigate the resource adequacy requirements of the PJM Interconnection, and the sensitivity of capacity procurement decisions to the choice of reliability metric used to measure resource adequacy. Assuming that plants fail independently, we find that PJM's 2010 reserve margin of 20.5% was sufficient to achieve the stated reliability standard of one loss of load event per ten years, with 0.012 expected loss of load events per year. PJM could reduce reserve margins to 13% and still achieve adequate levels of reliability as measured by the 2.4 Loss of Load Hours metric and the 0.001% Unserved Energy metric, which are used by other U.S. and international systems. A reserve margin of 13–15% would minimize long-run system costs. Reducing reserve margins from 20.5% to 13% in 2010 would have reduced PJM's capacity procurement by 11 GW, the same amount of coal capacity that PJM has identified as at high risk of retirement. We also investigate the risk posed by correlated failures among generators, a risk traditionally not modeled by system planners. We illustrate that three types of correlated failures may increase outage risks: natural gas supply disruptions, reduced reliability among generators during winter months, and the simultaneous shutdown of multiple nuclear generators for regulatory reasons. - Highlights: • We model resource adequacy in the PJM Interconnection. • If plant failures are independent, PJM could retire 11 GW of “at-risk” coal. • If plant failures are correlated, risks of supply shortages may be high.
[en] We investigate the economic viability of coupling a wind farm with compressed air energy storage (CAES) to participate in the day-ahead electricity market at a time when renewable portfolio standards are not binding and wind competes freely in the marketplace. In our model, the CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Other sources of revenue (capacity markets, ancillary services, price arbitrage) are not included in the analysis. We present a model to calculate profit maximizing day-ahead dispatch schedules based on wind forecasts. Annual profits are determined with dispatch schedules and actual wind generation values. We find that annual income for the modeled wind–CAES system would not cover annualized capital costs using market prices from the years 2006 to 2009. We also estimate market prices with a carbon price of $20 and $50 per tonne CO2 and find that revenue would still not cover the capital costs. The implied cost per tonne of avoided CO2 to make a wind–CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices. - Highlights: ► We modeled a wind farm participating in the day-ahead electricity market. ► We calculated optimal day-ahead market offers based on wind forecasts. ► Revenue is then calculated using measured wind power. ► We find that revenue is insufficient to cover capital costs at current market prices.
[en] Compressed air energy storage (CAES) could be paired with a wind farm to provide firm, dispatchable baseload power, or serve as a peaking plant and capture upswings in electricity prices. We present a firm-level engineering-economic analysis of a wind/CAES system with a wind farm in central Texas, load in either Dallas or Houston, and a CAES plant whose location is profit-optimized. With 2008 hourly prices and load in Houston, the economically optimal CAES expander capacity is unrealistically large - 24 GW - and dispatches for only a few hours per week when prices are highest; a price cap and capacity payment likewise results in a large (17 GW) profit-maximizing CAES expander. Under all other scenarios considered the CAES plant is unprofitable. Using 2008 data, a baseload wind/CAES system is less profitable than a natural gas combined cycle (NGCC) plant at carbon prices less than $56/tCO2 ($15/MMBTU gas) to $230/tCO2 ($5/MMBTU gas). Entering regulation markets raises profit only slightly. Social benefits of CAES paired with wind include avoided construction of new generation capacity, improved air quality during peak times, and increased economic surplus, but may not outweigh the private cost of the CAES system nor justify a subsidy. - Research highlights: → Sizes of CAES and transmission paired with a Texas wind farm are optimized for profit. → A profit-maximizing wind farm owner would not invest in a dedicated CAES system. → The social benefit of a wind/CAES system is unlikely to outweigh private cost. → CAES cannot cost-effectively smooth wind power with plausible imminent carbon prices.
[en] The location of a new electric power generation system with carbon capture and sequestration (CCS) affects the profitability of the facility and determines the amount of infrastructure required to connect the plant to the larger world. Using a probabilistic analysis, we examine where a profit-maximizing power producer would locate a new generator with carbon capture in relation to a fuel source, electric load, and CO2 sequestration site. Based on models of costs for transmission lines, CO2 pipelines, and fuel transportation, we find that it is always preferable to locate a CCS power facility nearest the electric load, reducing the losses and costs of bulk electricity transmission. This result suggests that a power system with significant amounts of CCS requires a very large CO2 pipeline infrastructure