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QR Utility Portfolio Optimization

QR Generation Optimization

QR Bidding Optimization to ISOs


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First real-time Power Optimization Platform that can boast a staggering rate of 12% extra profit on generation, or savings on energy portfolio procurement cost

Ready-to-use solution, no development or customization. We configure your assets in a few weeks and you go live. 24/7 Live Cloud Supercomputer Service, or on-site implementation. Pay 1 single yearly fee. All you need is a browser.
Seamless integration of optimization within our real-time intraday Trading Platform: data and analytics, load & price forecasting. Dashboards: bidding, scheduling, fuel nomination. Nodal load and P&L assessment and reporting.
One single Optimization Platform covering all perspectives: buy side, energy portfolio procurement for utilities, co-ops, retailers and marketers. Sell side: power plants' generation and bidding to ISOs, for thermal and hydro plants.
Business intelligence. Real-time visibility and performance analysis. Live Trading Panel displays and compares, intraday, (sub)-hourly nodal P&L and positions for actual, dispatched and optimal trading, generation and bidding strategies.

Solution and Benefits to Clients

QR Power Portfolio Optimization solutions go well beyond unit commitment whose objective is to solely minimize generation cost irrespective of market prices, or planning tools which produce average generation plans. Our optimization objective is to maximize net profit from generation, bidding or procurement versus buy and sale opportunities in ISO nodal spot markets.

Our optimization solutions is delivered within a real-time electricity trading and scheduling platform integrating business intelligence, load and price forecasting, trading performance analysis comparing the dispatch, MQ and P&L of actual and optimal procurement, generation and bids to the ISO.

Two Comprehensive Power Portfolio Optimization Solutions:

QR Utility Optimization
QR Utility Optimization
Utility Portfolio Optimization. Optimal (sub)-hourly dispatch, across a portfolio of bilateral contracts and ISO markets; minimizes energy procurement cost. Real-time & day ahead electricity scheduling and trading dashboard comparing actual, forecast and optimal nodal load, price and P&L’s.

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QR Generation & Bidding Optimization
QR Generation & Bidding Optimization
Generation and Bidding Optimization. Maximize generation profits. Optimal (sub)-hourly generation schedule and optimal price bidding to ISO. Real-time and day ahead electricity trading, scheduling and bidding dashboard comparing actual, forecast and optimal data: load, price, bid, and P&L.

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QR Utility Portfolio Optimization

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    Power Portfolio Optimization Features & Benefits
    • Lower power procurement costs by 12% on the average. Real-time and day-ahead electricity bilateral procurement portfolio and ISO.
    • Electric utilities, cooperatives and retail marketers have diverse energy procurement portfolios composed of buy positions from multiple financial and physical bilateral contracts or PPAs with different suppliers and generators, together with the possibility of buying and selling the imbalance to a spot or external market.
    • QR Utility Portfolio Optimization solution minimizes total power procurement costs and rate, by taking full advantage of arbitrage and market opportunities arising from price moves in volatile electricity spot markets, and the flexibilities built in the bilateral energy procurement contracts.
    • A single real-time electricity trading platform integrating optimization, scheduling, data, analytics, load and price forecasting, along with nodal load positions and P&L.
    • Detailed forecast of load requirements, cash flow and P&L of bilateral procurements, ISO buy and sale. What-if-scenario analysis.
    • (Sub)-hourly electricity procurement portfolio and ISO imbalance management.
    • Automated electronic scheduling of power procurement contracts across suppliers and ISO spot markets.
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    Optimization Objective. A utility, cooperative, or energy marketer can have a diverse energy procurement portfolio composed of multiple financial and physical bilateral contracts or PPAs with different generation companies, together with the possibility of buying or selling the imbalance to one or more spot markets. QR Utility Optimization is a real-time solution which computes for every (sub)-hourly forward period, the optimal allocation of load to each procurement contract and the spot market with the objective to minimize the total energy cost across a full day.
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    Optimization Methodology. We model the procurement contracts as a (sub)-hourly portfolio of spark spread options. Optimization minimizes energy procurement cost by trading real-time, each bilateral procurement contract, in and out of one or more mitigation or spot markets, subject to all contractual, operational, and the global demand load constraints. For example, in a period where the forecasted spot market price is lower than the cost of some bilateral contracts, optimization minimizes purchases from these bilateral contracts and buys from the spot market to satisfy the forecasted load, subjected to all constraints. Conversely, when the forecast spot price is higher than some bilateral contacts, optimization maximizes purchase from the bilateral contracts and sells to the spot market, satisfying the forecasted demand and all constraints. The presence of constraints renders this optimization problem path-dependent across a 24 hour forward window, as explained below.
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    Contractual Complexities and Optimization. Compared to generation assets, physical energy procurement contracts can have far more complex constraints and degrees of flexibility. These are precisely the reasons why the dispatch of such contracts can be optimized. The resulting decision tree of possible interlinked dispatch states can grow exponentially large, rendering the problem manually intractable.
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      Contracts with a (sub)-hourly, daily or monthly must take-or-pay quantity, while allowing the load to vary between specified min and max quantities. Similarly, time of use (sub)-hourly contracts allowing variability in the load can be optimized. The dispatch decision at any period, impacts the remaining dispatch options for the subsequent periods of the day. As a result, this is a path-dependent optimization problem. That is, one can’t optimize single periods independently, but consecutive periods such as an entire day must be optimized together, based on a short term load and price forecast.
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      Level dependent ramp up and down constraints are handled. These increase exponentially the allowable states the optimization process must traverse, causing serious numerical challenges we have overcome.
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      Meeting total demand load forecast serves as the global constraint of optimization at every period.
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      Complex pricing mechanisms and fuel costs can be easily configured in the model in a user friendly cash flow mapping dialog. Typically, plants’ detailed operational costs and nonlinear heat rate curve are not part of energy procurement contracts' specifications. If they were however, they could be easily added into the model as it is done with generation assets. The introduction of a heat-rate curve renders this a nonlinear optimization problem of the load.
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      We do not use closed form linear or quadratic optimization solutions. To capture all above listed features we have developed a proprietary hybrid nonlinear optimization methodology implemented via mix-integer constrained programming.
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    Short-term Real-time Tactical Optimization of Electricity Trading
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      Short-term power procurement portfolio optimization minimizing total energy procurement costs in real-time or day-ahead electricity markets, from the next period up to a week-ahead.
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      Real-time solution. At every market period, the following chain is executed automatically: nodal prices are updated, electricity spot price forecasting is redone, and optimization is executed to determine (sub)-hourly optimal buy allocation across the power procurement portfolio and corresponding buy-sell of the imbalance in the ISO spot market.
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      The above cycle is repeated around the clock, at every market period, under a minute. Nodal electricity price forecasts have higher precisions for next few periods and the resulting optimal strategies fully captures market opportunities at a high confidence level. Traders have ample time to revise (sub)-hourly optimal scheduling every period.
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    Optimization of Power Portfolio Planning
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      Medium to long-term hourly optimization of electricity procurement portfolios.
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      Optimization objective is to minimize total electricity procurement costs, when planning the allocation of resources across the energy portfolio.
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      Optimization and what-if-scenario projections over long horizons are the best tools to optimally price a power procurement contract or PPA. E.g., define different pricing rules, or remove or add some constraints to increase or limit some contractual flexibilities. Then execute optimization on these scenarios to measure and compare the effect on the cash flow of each electricity procurement contract. The bilateral power procurement portfolio with the lowest energy procurement cost, taking into account buy-sell opportunities in and out of the spot market, is the optimal power portfolio.
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      The above represents the total value of the electricity procurement portfolio. Explore and price various hedging programs.
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    Frequently Asked Questions
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      Can we optimize only 1 bilateral procurement contract? Yes as long as you can buy from and sell to an ISO or some external market. Optimization has nothing to do with the number of bilateral contracts, but rather with the ability to trade them in and out of other markets.
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      Which bilateral procurement contract can’t be optimized? A bilateral procurement contract with no flexibility in the load, i.e., a take or pay with fixed (sub)-hourly load can’t be optimized as dispatch can’t be modified across periods.

QR Generation Optimization

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    QR Power Generation Optimization
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      Short-term (sub)-hourly power generation and bidding optimization increases electricity generation profits by 12%. This optimization is applicable both to real-time and day-ahead electricity markets and ISOs.
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      Our power plant optimization solution is far more sophisticated than unit commitment tools whose objective is to solely minimize cost irrespective of market prices, or planning tools which produce average long term plant operational plans without solving the problem of bidding to ISO spot markets. QuantRisk's objective of optimization is to maximize net profit from electricity generation and bidding for sale to ISO or bilateral power markets.
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      Real-time intra-day or day-ahead (sub)-hourly trading. Our solution allows traders to continually adjust power generation and bidding strategies to take full advantage of arbitrage and market opportunities arising from price moves in volatile electricity sport and bilateral markets. At every market period, the following chain is executed automatically: electricity nodal prices are updated, nodal spot price forecasting is redone, and optimization is executed to determine (sub)-hourly optimal generation plan, followed by the optimization of corresponding bids to the ISO for next periods, day and week-ahead.
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      Optimization of power generation assets can be also used in planning for medium to long-term horizons. Power plant optimization integrates energy, reserves and transmission. Explore optimal planning for generation, fuel and repairs, water reservoir and release under diverse combinations of what-if scenarios on electricity market conditions and rules, nodal electricity prices, fuel prices, outages, congestions, weather and rain.
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    Optimization Methodology and Objective for Thermal Plants.
    Define any desired optimization objective function in a user friendly interface.
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      We model thermal generation as a (sub)-hourly portfolio of spark spread options. We first forecast prices, and based on the forecast, optimization maximizes generation profit by trading real-time, generation in and out of one or more mitigation or spot markets, subject to operational constraints. Examples:
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      Downside risk. An asset is said to be out of the money in forward periods where the forecasted spot market price is lower than total generation cost. In this case optimization minimizes generation subjected to all constraints, and buys from the spot market to satisfy its committed load on the sales side if any.
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      Upside opportunity. An asset is said to be in the money when the forecast spot price is higher than generation cost. In this case optimization maximizes generation, subject to all operational constraints, and sells energy to the spot market.
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      The presence of constraints, especially ramp rate, renders this optimization problem path-dependent across a 24 hour forward window, as explained below.
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    Optimization Methodology and Objective for Hydro Plants.
    Define any desired optimization objective function in a user friendly interface.
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      We model hydro generation as a (sub)-hourly portfolio of constrained path dependent time spread call options on the spot price. We first forecast prices, and based on the forecast, optimization maximizes total generation profit over a day, by generating at the collection of highest priced forecasted spot prices periods, subject to water release availability, schedule and rates, and the plant’s operational constraints.
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      The operational parameters and constraints of the plants’ operational levels, ramp, constraints, schedule of water flow and release, conversion rule and rate of water to electricity, etc.
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    Modeling Complexities and Optimization. You can define any desired complex nonlinear cost structure, including start and stop, and mixed fuel costs. Any nonlinear heat rate curve defined for the plant automatically induces nonlinearity in the corresponding fuel cost. You can define operational costs as any function of the plant's run-time, or the generated quantity.
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      You can literally define any desired operational constraint: must run, min and max (sub)-hourly and daily generation, shut down schedules, forced discreet generation levels to choose from, ability to adjust plant’s capacity per periods.
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      Level dependent nonlinear heat-rate curves for thermal plants, and water to power conversion curves for hydro plants. No formulas or rules, you provide any desired curve and the system can interpolate to smooth out the curve.
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      Level dependent ramp-up and down rates.
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      Because of constraints, and notably ramp, the generation decision at any period, impacts the remaining generation options for the subsequent periods of the day. As a result, this is a path-dependent optimization problem. That is, one can’t optimize single periods independently, but consecutive periods such as an entire day must be optimized together, based on short term price forecast. These increase exponentially the allowable states the optimization process must traverse, causing serious numerical challenges we have overcome.
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      Modeling costs. A cash flow mapping interface allows to easily define any desired complex generation cost structure, mixed fuel and multiple currencies. Level dependent nonlinear heat-rate curves defined for the plant automatically induces nonlinearity in the corresponding fuel cost. Operational costs as a function of the plant's run-time, and generated quantity. Start and stop costs.
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      Modeling Revenue. A cash flow mapping interface allows to easily define the revenue side by selling generation at injection nodes, or at a uniform trading hub or zonal price.
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      The introduction of a heat-rate curve renders this a nonlinear optimization problem of the load. We do not use closed form linear or quadratic optimization solutions. To capture all above listed complexities and nonlinearities we have developed a proprietary hybrid nonlinear optimization methodology implemented via mix-integer constrained programming.
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    Fuel and Pollutant Management
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      Automated calculation of multiple fuel input required per type, and multiple emissions outputs, SO2/NOX/CO2.
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      Automated gas nomination corresponding to nominal and optimal dispatch load.
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    Real-time Tactical versus Planning Optimization.
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      Tactical Deterministic Optimization. The key input to generation optimization is price forecasts. We offer very accurate short term price forecast, which is recomputed (sub)-hourly around the clock. Generation optimization can thus generate profits with high degree of accuracy if used dynamically every (sub)-hourly period within a day. Generation decisions are typically made 1 to 2 hours before a period, and thermal plants may take one to two periods to ramp up or down. In short, we recommend use this solution tactically, every period to decide about generation for the next allowable few periods, and repeat this around the clock. This is dynamic real-time trading and optimization. This solution runs in minutes every (sub)-hourly period.
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      Strategic Stochastic Optimization. As we go forward beyond a few days, the accuracy of the price forecast diminishes. We offer a (sub)-hourly Monte Carlo Simulation of nodal electricity spot prices with the mean being the forecast. The optimization uses the price simulation scenarios to create a distribution around (sub)-hourly generation and its P&L. This solution is appropriate for mid to long term planning purposes. It runs once a day.
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    Frequently Asked Questions.
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      Can a generation asset under contracts be optimized, or just merchant plant? We can only optimize the variable revenue that we control, that is generation and bidding. Bilateral sales are fixed revenue and don’t affect the optimization objective. All generation assets can be optimized, whether merchant or contacted. When the asset is in the money optimization wants the plant to generate to maximum capacity subject to all constraints, and sell for profit to the ISO. The extra profit is due to the quantity exceeding the bilateral sales commitment. On the flip side, when the price forecast is lower than generation cost and the asset is out of money, optimization lowers generation to the extent possible to avoid losses, and buys from the market to satisfy the bilateral sales. In short, if your plant is 200 MWh, and 50 MWh are under bilateral contracts. It is the whole 200 MWh we optimize, and not just the 150 MWh.
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      What type of power plant is optimizable? Optimization requires generation flexibility, which depends to a large degree on ramping ability. Faster ramping plants are more suitable for optimization. On the cost side, optimization is more valuable for plants with high fuel costs. Peaking hydro, gas and oil plants are prime candidate for optimization on the upside. These can be merchant or committed to some load contracts. Mid-merit plants can also be optimized especially when committed to some sales contracts. During off-peak hour when the plant is out of the money, it is optimal to shut the plant down and buy from the market to satisfy the load contract. This not only saves on fuel but on wear and tear and repairs. Thanks to its precise price forecasting, the optimization will ramp up to generate on time for when it turns in the money.
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      How much generation capacity can be optimized? Any generation MW quantity that can be ramped up/down in 2 hrs. and whose bidding to an ISO you control. E.g., a 500 MW plant (hydro, gas, oil, coal) with a 60 MW/hr. ramp can be optimized for 120 MW.

QR Bidding Optimization to ISOs

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    Power plants must submit bids in multiple bands or blocks to their ISO, RTO or power pool. Bids and offers are used to build a stack to derive the marginal plant, which determines the spot, ancillary and reserve prices for a given period. This applies both to real-time and day-ahead power markets. E.g., many electricity markets use 10 bands or blocks for energy bids. Electricity generators typically repeat the same or similar static bids, some being successful and some missing the marginal bid. Over and underperforming bids lead to serious losses, which can all be eliminated by our optimization solution.
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    QR Market Bidding Optimization implements a proprietary global optimization method which determines for every period, the optimal set of bids for a generator that maximize net profit from generation’s sale to the ISO spot market. The optimal bids jointly maximize dispatch load, outbid other competitors’ bids, and when possible, attempt to increase market prices by moving the marginal bid higher in the stack of bids and offers.
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    Optimization Methodology and Objective. QR Market Bidding Optimization is automatically integrated with QR Generator Optimization.
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      First, QR Generator Optimization™ has determined the generation level for every forward period the asset is forecasted to be in the money and to operate.
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      Secondly, we forecast and simulate forward the stack of bids for the entire market with our proprietary model. Every bid we put into this stack will displace the marginal bid, and lower or increase the settled price of a given period.
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      Finally, the bidding optimization bootstraps bids for the plant (across multiple bands) through the simulated stack of bids and offers with the objective to determine the set of bids that maximizes sales profit from the generated quantity to the market. This is done simultaneously for all forward periods across a day. This has for effect to jointly maximize dispatch load and market prices.
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    Modeling Complexities and Optimization.
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      The sophisticated hybrid stochastic and evolutionary optimization mathematical model allows multi-objective functions to either combine different optimization perspectives for a single generator, or to model multiple generators of the same company in the market.
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      You can adjust with a slider, whether you wish to bid the plant as a market taker, or market maker, or somewhere in the middle. Market taker bids are optimally placed below the marginal bid to ensure full dispatch, and taking the forecast price, by displacing other competitor bids. Market maker bids take calculated risk by placing some bids, of smaller quantities, at higher prices while withholding some generation quantity in order to push prices higher.