16,355 research outputs found
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Locational-based Coupling of Electricity Markets: Benefits from Coordinating Unit Commitment and Balancing Markets
We formulate a series of stochastic models for committing and dispatching electric generators subject to transmission limits. The models are used to estimate the benefits of electricity locational marginal pricing (LMP) that arise from better coordination of day-ahead commitment decisions and real-time balancing markets in adjacent power markets when there is significant uncertainty in demand and wind forecasts. The unit commitment models optimise schedules under either the full set of network constraints or a simplified net transfer capacity (NTC) constraint, considering the range of possible real-time wind and load scenarios. The NTC-constrained model represents the present approach for limiting day-ahead electricity trade in Europe. A subsequent redispatch model then creates feasible real-time schedules. Benefits of LMP arise from decreases in expected start-up and variable generation costs resulting from consistent consideration of the full set of network constraints both day-ahead and in real-time. Meanwhile, using LMP to coordinate adjacent balancing markets provides benefits because it allows intermarket flow schedules to be adjusted in real-time in response to changing conditions. These models are applied to a stylised four-node network, examining the effects of varying system characteristics on the magnitude of the locational-based unit commitment benefits and the benefits of intermarket balancing. Although previous www.eprg.group.cam.ac.uk EPRG WORKING PAPER studies have examined the benefits of LMP, these usually examine one specific system, often without a discussion of the sources of these benefits, and with simplifying assumptions about unit commitment.
We conclude that both categories of benefits are situation dependent, such that small parameter changes can lead to large changes in expected benefits. Although both can amount to a significant percentage of operating costs, we find that the benefits of balancing market coordination are generally larger than the unit commitment benefits
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Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review
YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000
Adding renewables to the grid: Effects of Storage and Stochastic Forecasting
The electricity sector contributes to a quarter of global greenhouse emissions, and managing its evolution is a critical sustainability challenge. The context for the development and operation of electricity grids has dramatically changed in recent years. Wind and solar power have become much less expensive. Lower costs combined with increased policy action to address carbon emissions is leading to substantial shares of electricity generated by intermittent renewables. Maintaining a stable electricity supply with intermittency is a critical challenge; storage and natural gas are possible solutions. While policymakers promote storage as green grid technology, low-cost natural gas from hydrofracturing extraction raises the economic hurdle for storage.
Researchers have developed complicated energy system models to help plan grids in the face of the above trends. The research in this dissertation introduces new modeling features that affect the economic and environmental outcomes of the adoption of renewable and storage technologies. First, prior models that explore the future build-out of electricity grids are nearly always deterministic, i.e., they assume that decision-makers have perfect information. Here a stochastic optimization grid expansion model is developed that presumes that expected future fluctuations, e.g. in fuel prices, influence build-out decisions. This stochastic model thus includes uncertainty and risk as core elements: Grid build-out depends on the distribution of system costs. A genetic algorithm with Monte-Carlo simulation is used for co-optimization using two objective functions: “risk-neutral,” which optimizes to minimize average system cost and “risk-averse,” which optimizes to minimize average of the top 5% of costs (also called 95% Conditional Value at Risk (CVaR)). This model is tested for the US Midwest regional grid. The results show that the risk-averse scenario does not increase mean system costs but adds significantly more wind. These results corroborate prior work showing that electricity system costs can be surprisingly inelastic to renewable adoption and further introduces quantification of how increased renewables lowers cost risk.
Second, the economic and environmental performance of storage is complicated by how its introduction affects the operation of both renewable and fossil plants. In this dissertation, a model is developed that accounts for how storage operation would affect prices on the grid and in turn, the operational schedule that yields optimal revenue. Results from modeling the US Midwest region shows that this treatment of storage as a “price maker” affects results. The model indicates that storage increases carbon emissions when it enables a high emissions generator, such as a coal plant, to substitute for a cleaner plant, such as natural gas. In this case, low cost; efficient natural gas generation is relatively better than coal to realize emissions reductions with storage under economic arbitrage until renewables dominate the grid mix.
Third, the operational strategies of energy storage alter the generation and profits of the other electricity generation systems. The operational effects of storage on the change in generation is investigated for all the eGRID subregions across the US based on actual historical electricity prices and the generation mix for the year 2016. Results show that storage increases the coal generation and affects the natural gas generation in the west – except in California and the Midwest regions of the US; and increases the generation of the natural gas in the eastern US regions. California, upstate New York and New England regions show an exception with an increase in natural gas generation and decrease in coal generation. The model also investigates the operational effects of storage on the profits of other generating units in California, Midwest and New York regions. Profits of other generating units are significantly affected when large capacities of storage operate as price-makers. Coal has a small increase in profits by 2% and all the other fuels continue to see a decline in profits in New York and the Midwest regions. The decrease in profits of the other generating units is because of the offset/retirements of the peaker natural gas plants that set the electricity prices. On the other hand, in California, the profits for renewables increase from the increase in electricity clearing prices set by the natural gas combined cycle plants to meet the additional demand from the storage charging
Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability
The integration of large-scale renewable generation has major implications on
the operation of power systems, two of which we address in this work. First,
system operators have to deal with higher degrees of uncertainty due to
forecast errors and variability in renewable energy production. Second, with
abundant potential of renewable generation in remote locations, there is an
increasing interest in the use of High Voltage Direct Current lines (HVDC) to
increase transmission capacity. These HVDC transmission lines and the
flexibility and controllability they offer must be incorporated effectively and
safely into the system. In this work, we introduce an optimization tool that
addresses both challenges by incorporating the full AC power flow equations,
chance constraints to address the uncertainty of renewable infeed, modelling of
point-to-point HVDC lines, and optimized corrective control policies to model
the generator and HVDC response to uncertainty. The main contributions are
twofold. First, we introduce a HVDC line model and the corresponding HVDC
participation factors in a chance-constrained AC-OPF framework. Second, we
modify an existing algorithm for solving the chance-constrained AC-OPF to allow
for optimization of the generation and HVDC participation factors. Using
realistic wind forecast data, for 10 and IEEE 39 bus systems with HVDC lines
and wind farms, we show that our proposed OPF formulation achieves good in- and
out-of-sample performance whereas not considering uncertainty leads to high
constraint violation probabilities. In addition, we find that optimizing the
participation factors reduces the cost of uncertainty significantly
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