183 research outputs found
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Impacts of variable renewable energy on wholesale markets and generating assets in the United States: A review of expectations and evidence
We synthesize available literature, data, and analysis on the degree to which growth in variable renewable energy (VRE) has impacted or might in the future impact bulk power system assets, pricing, and costs in the United States. Most studies of future scenarios indicate that VRE reduces wholesale energy prices and capacity factors of thermal generators. Traditional baseload generators are more exposed to these changing market conditions than low-capital cost and more flexible intermediate and peak-load generators. From analysis of historical data we find that VRE is already influencing the bulk power market through changes in temporal and geographic patterns areas with higher levels of VRE. The most significant observed impacts have concentrated in areas with significant VRE and/or nuclear generation along with limited transmission, with negative pricing also often occurring during periods with lower system-wide load. So far, however, VRE, has had a relatively modest impact on historical average annual wholesale prices across entire market regions, at least in comparison to other drivers. The reduction of natural gas prices is the primary contributor to the decline in wholesale prices since 2008. Similarly, VRE impacts on thermal plant retirements have been limited and there is little relationship between the location of recent retirements and VRE penetration levels. Although impacts on wholesale prices have been modest so far, impacts of VRE on the electricity market will be more significant under higher VRE penetrations
Impact of Demand Response on Thermal Generation Investment with High Wind Penetration
We present a stochastic programming model for investments in thermal generation capacity to study the impact of demand response (DR) at high wind penetration levels. The investment model combines continuous operational constraints and wind scenarios to represent the implications of wind variability and uncertainty at the operational level. DR is represented in terms of linear price-responsive demand functions. A numerical case study based on load and wind profiles of Illinois is constructed with 20 candidate generating units of various types. Numerical results show the impact of DR on both investment and operational decisions. We also propose a model in which DR provides operating reserves and discuss its impact on lowering the total capacity needed in the system. We observe that a relatively small amount of DR capacity is sufficient to enhance the system reliability. When compared to the case with no DR, a modest level of DR results in less wind curtailment and better satisfaction of reserve requirements, as well as improvements in both the social surplus and generator utilization, as measured by capacity factors
Multi-Stage Decision Rules for Power Generation & Storage Investments with Performance Guarantees
We develop multi-stage linear decision rules (LDRs) for dynamic power system
generation and energy storage investment planning under uncertainty and propose
their chance-constrained optimization with performance guarantees. First, the
optimized LDRs guarantee operational and carbon policy feasibility of the
resulting dynamic investment plan even when the planning uncertainty
distribution is ambiguous. Second, the optimized LDRs internalize the tolerance
of the system planner towards the stochasticity (variance) of uncertain
investment outcomes. They can eventually produce a quasi-deterministic
investment plan, which is insensitive to uncertainty (as in deterministic
planning) but robust to its realizations (as in stochastic planning). Last, we
certify the performance of the optimized LDRs with the bound on their
sub-optimality due to their linear functional form. Using this bound, we
guarantee that the preference of LDRs over less restrictive -- yet poorly
scalable -- scenario-based optimization does not lead to financial losses
exceeding this bound. We use a testbed of the U.S. Southeast power system to
reveal the trade-offs between the cost, stochasticity, and feasibility of
LDR-based investments. We also conclude that the LDR sub-optimality depends on
the amount of uncertainty and the tightness of chance constraints on
operational, investment and policy variables
Temporal vs. Stochastic Granularity in Thermal Generation Capacity Planning with Wind Power
We propose a stochastic generation expansion model, where we represent the long-term uncertainty in the availability and variability in the weekly wind pattern with multiple scenarios. Scenario reduction is conducted to select a representative set of scenarios for the long-term wind power uncertainty. We assume that the short-term wind forecast error induces an additional amount of operating reserves as a predefined fraction of the wind power forecast level. Unit commitment (UC) decisions and constraints for thermal units are incorporated into the expansion model to better capture the impact of wind variability on the operation of the system. To reduce computational complexity, we also consider a simplified economic dispatch (ED) based model with ramping constraints as an alternative to the UC formulation. We find that the differences in optimal expansion decisions between the UC and ED formulations are relatively small. We also conclude that the reduced set of scenarios can adequately represent the long-term wind power uncertainty in the expansion problem. The case studies are based on load and wind power data from the state of Illinois
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