Search

Wind and solar power are highly variable, so it is it unclear how large a role they can play in future power systems. This work introduces a new open-source electricity planning model-Switch-that identifies the least-cost strategy for using renewable and conventional generators and transmission in a large power system over a multidecade period. Switch includes an unprecedented amount of spatial and temporal detail, making it possihle to address a new type of question about the optinsal design and operation of power systems with large amounts of renewable power. A case study of California for 2012-2027 finds that there is no masisnum possible penetration of wind and solar power-these resources could potentially be used to reduce emissions 9096 or more below 1990 levels without reducing reliability or severely raising the cost of electricity. This work also finds that policies that encourage customers to shift electricity demand to times when renewable power is most abundant (e.g., well-timed charging of electric vehicles) could make it possible to achieve radical emission reductions at moderate costs. [ABSTRACT FROM AUTHOR]

Abstract: Camelina sativa (L.) is a promising crop for biodiesel production that avoids many of the potential pitfalls of traditional biofuel crops, such as land use change (LUC) and food versus fuel. In this study the environmental viability of camelina biodiesel was assessed using life cycle analysis (LCA) methodology. The LCA was conducted using the spreadsheet model dubbed KABAM. KABAM found that camelina grown as a niche filling crop (in rotation with wheat or as a double crop) reduces greenhouse gas (GHG) emissions and fossil fuel use by 40–60% when compared to petroleum diesel. Furthermore, by avoiding LUC emissions, camelina biodiesel emits fewer GHGs than traditional soybean and canola biodiesel. Finally, a sensitivity analysis concluded that in order to maintain and increase the environmental viability of camelina and other niche filling biofuel crops, researchers and policy makers should focus their efforts on achieving satisfactory yields (1000–2000kg/ha) while reducing nitrogen fertilizer inputs. [Copyright &y& Elsevier]

Wind farms provide electricity with no direct emissions. However, their output cannot be forecasted perfectly, even a short time ahead. Consequently, power systems with large amounts of wind power may need to keep extra fossil-fired generators turned on and ready to provide power if wind farm output drops unexpectedly. In this work, I introduce a new model for estimating the uncertainty in short-term wind power forecasts, and how this uncertainty varies as wind power is aggregated over larger regions. I then use this model to estimate the reserve requirements in order to compensate for wind forecast errors to a 99.999% level of reliability, and an upper limit on the amount of carbon dioxide that would be emitted if natural gas power plants are used for this purpose. I find that for regions larger than 500 km across, operating reserves will undo 6% or less of the greenhouse gas emission savings that would otherwise be expected from wind power. [ABSTRACT FROM AUTHOR]

Wind power production is variable, but also has diurnal and seasonal patterns. These patterns differ between sites, potentially making electric power from some wind sites more valuable for meeting customer loads or selling in wholesale power markets. This paper investigates whether the timing of wind significantly affects the value of electricity from sites in California and the Northwestern United States. We use both measured and modeled wind data and estimate the time-varying value of wind power with both financial and load-based metrics. We find that the potential difference in wholesale market value between better-correlated and poorly correlated wind sites is modest, on the order of 5%-10%. A load-based metric, power production during the top 10% of peak load hours, varies more strongly between sites, suggesting that the capacity value of different wind projects could vary by as much as 50% based on the timing of wind alone. [ABSTRACT FROM AUTHOR]

Abstract: This research quantifies the extent to which the US has shifted the environmental impact associated with the goods it consumes to other countries through trade. To achieve this, we use a life-cycle, consumption-based approach to measure the environmental impacts embodied in US trade activities for global warming potential (GWP), energy, toxics, and the criteria air pollutants. We use these values to determine the amount of environmental impact “leaked” from current, production-based approaches to analyzing national environmental trends for the years 1998–2004. We find that in 2004, with reasonable assumptions about the environmental intensity of imports and exports, this leakage exceeds 10% for all studied impacts, exceeds 20% for GWP, energy, and most criteria air pollutants, and exceeds 80% for lead emissions and toxics. By including the environmental impacts embodied in trade activities into national environmental accounts, we provide consumption-based, US per capita, environmental impacts, which we use to evaluate the relationship between income and environmental impact. We find evidence for rising per capita environmental impacts over time in the US, contra the Environmental Kuznets Curve. The paper concludes with a discussion of the implications for international environmental policy of increasing embodied emissions in trade. [Copyright &y& Elsevier]

A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants' willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a "brick wall" preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar "brick-wall" MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the "brick-wall" approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the "brick-wall" strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. [ABSTRACT FROM AUTHOR]

This paper proposes two parametric probabilistic forecast methods using beta and two-sided power distributions to predict solar irradiance. It also evaluates their performance. To improve their performance metrics a hybrid procedure based on the beta transformed linear opinion pool is utilized. Our simulations show that these methods – despite their simple structure – can effectively forecast solar irradiance and accurately describe its stochastic characteristics. The proposed approach is flexible and could be extended to many different point forecast methods which otherwise minimize RMSE or MSE. [ABSTRACT FROM AUTHOR]

The use of renewable energies, such as wind and solar, is rising in microgrids. However, because of their intermittent nature, achieving a stable system in spite of the fluctuations of generation and demand is very difficult. Aiming to increase the stability margin of AC islanded microgrids comprising nondeterministic resources, properly share real power (P), and keep the network frequency (f) at nominal value, this study exploits the presence of energy storage units (ESUs) along with a load-shedding scheme. To this end, this study proposes a multitier decentralized control strategy that is based on frequency and includes three control schemes, distributed generation unit (DGU) control, ESU control, and local loads control (LLC). These controllers operate simultaneously. The DGU controller shares the loads among DGUs in accordance with the frequency droop method. On the other hand, the ESU controller controls the production of ESUs and also the LLCs manage the loads and dump-load of the microgrid. This method ensures the proper operation of AC microgrids that comprise intermittent resources under stand-alone conditions and despite numerous events. The time responses of P and f prove the high performance of the method. The simulations are performed and coded in m-file of MATLAB. [ABSTRACT FROM AUTHOR]

In electric power grids, generation must equal load at all times. Since wind and solar power are intermittent, system operators must predict renewable generation and allocate operating reserves to mitigate imbalances. If they overestimate the renewable generation during scheduling, insufficient generation will be available during operation, which can be very costly. However, if they underestimate the renewable generation, usually they will only face the cost of keeping some generation capacity online and idle. Therefore overestimation of renewable generation resources usually presents a more serious problem than underestimation. Many researchers train their solar radiation forecast algorithms using symmetric criteria like RMSE or MAE, and then a bias is applied to the forecast later to reflect the asymmetric cost faced by the system operator – a technique we call indirectly biased forecasting. We investigate solar radiation forecasts using asymmetric cost functions (convex piecewise linear (CPWL) and LinEx) and optimize directly in the forecast training stage. We use linear programming and a gradient descent algorithm to find a directly biased solution and compare it with the best indirectly biased solution. We also modify the LMS algorithm according to the cost functions to create an online forecast method. Simulation results show substantial cost savings using these methods. [ABSTRACT FROM AUTHOR]

This paper reports a new control strategy to improve sharing of unbalanced currents in islanded LV microgrids. This technique provides fast and effective sharing of positive-, negative- and zero-sequence currents, and is the first example of zero-sequence current sharing in the literature. The controllers are designed in the stationary frame. The control structure consists of four loops: (1) the current controller; (2) the voltage controller; (3) the droop controller and the (4) negative and zero sequence current controllers. The output current is considered unknown for the controller and is added to the control system as a disturbance. The proposed controller features a high gain in fundamental and harmonic frequencies, hence a good voltage quality is obtained in the presence of unbalanced and nonlinear loads. To this aim, a proportional-resonant (PR) controller is adopted as the current controller. By using a multi-resonant controller as current controller, a unified control structure is obtained which is suitable for both grid-connected and islanded modes. The voltage controller is designed using a resonant controller so that the voltage can have low VUF and THD in the presence of unbalanced and nonlinear loads. Furthermore, in this paper, the droop method is applied to the control structure to share real and reactive powers. Simulation studies show that the conventional droop method cannot share the oscillatory part of the output power that is due to the presence of unbalanced loads in the microgrid. This paper relies on using zero and negative sequence virtual impedance controller to share the oscillatory part of output power. By using zero-sequence virtual impedance controller (ZSVIC) and negative-sequence virtual impedance controller (NSVIC), the zero and negative sequence currents in the microgrid are controlled and shared effectively. By compensating zero- and negative-sequence currents locally, the flow of these currents in the microgrid is minimized, and the overall power quality of the islanded LV microgrid is improved. [ABSTRACT FROM AUTHOR]