Unit commitment software for generator scheduling and economic dispatch.

Powerop can be used for economic dispatch by fixing the on/off decisions of the generating units in the input data. It then optimizes power outputs and reserve provision for that unit commitment schedule, using the applicable plant and system constraints, including ramp rates, energy limits and system-wide or zonal reserve requirements.

This allows the same detailed plant and reserve models to be used for either integrated unit commitment and dispatch, or dispatch with a specified commitment schedule.

• The study period is divided into scheduling intervals chosen by the user, usually half-hourly or hourly, although shorter or longer intervals can also be used.
• For most power systems, studies of up to a week at half-hourly intervals can be solved to good accuracy within an acceptable run time. Longer studies, or very large systems, can use Powerop's Planner option.

Powerop can model the operation of thermal, hydro-electric and renewable generating units and electricity contracts with other companies. Thermal generating units can include coal-fired, oil-fired, gas-fired and nuclear-powered steam turbines, and combustion turbines burning distillate (which are often called gas turbines). Thermal units can also include dual-fired generating units, which can use one of two alternative fuel types. Powerop can model and optimize the use of mixtures of different fuels and/or gas contracts in the same generating unit or group of units. The hydro-electric units can include conventional hydro units and Pumped Storage units. Gas-fired units and hydro-electric units are treated as energy-limited plant. Energy storage units, such as Battery Energy Storage Systems (BESS) and pumped storage, can be modelled and their operation optimized. Upper or lower energy limits may also be applied to other types of generating units and may be used, for example, to represent limited fuel supplies or 'take or pay contracts'. Renewable generation, such as wind farms and solar power, can be modelled as pseudo-generating units with zero fuel costs and time-varying availabilities, which might represent the forecast total wind power or solar power in a particular region.

• For system scheduling, the total output of the generating units is matched to the forecast value of the system demand at each time-point. The penalty for not doing so is set by the user, so this constraint may be made 'hard' or 'soft', depending on the user's requirements.
• The total spinning-reserve from all the generating units must be greater than or equal to the spinning-reserve requirement of the system. This can be a fixed requirement in MegaWatts (MW), a specified percentage of the largest on-load output of any generating unit, or a combination of the two. Again, the user can choose how strongly to enforce this constraint. The purpose of the spinning-reserve requirement is to ensure that there is enough spare capacity from the generating units that are on-load or 'spinning' at any time to cover the accidental loss of any individual generating unit, or to satisfy demands that in practice are higher than their forecast values. The precise definition of how much spinning reserve a particular generating unit supplies can be customised to the user's requirements. Several different spinning reserve requirements may be co-optimized, with each reserve requirement being over a different time-scale.
• For systems with relatively few generating units, a loss-of-infeed reserve requirement can be specified as a percentage of unit output. It can apply to the largest running unit or to each running unit in turn, so that the reserve held on all the other units covers its sudden loss. The percentage can be set separately for each reserve category, for example below 100% where full cover would be uneconomic, and a MW requirement can be added to allow for demand or renewable uncertainty. Because this requirement is computationally demanding, it is best suited to smaller systems.
• Powerop can also model standing reserve. This is reserve that is provided by generating units that are currently off-load but which can be started-up and dispatched within a relatively short time, such as 10 minutes.
• Powerop can also impose a downward-reserve requirement. This ensures that there are sufficient generating units running above their Minimum Stable Generation levels at all times to allow the total output to be quickly reduced by a specified number of MW. (There may be a variety of reasons for such a requirement - for example to cover the possibility of demands in practice being lower than their forecast values.)
• Powerop can equalise the outputs of identical generating units at any one time, but only if this does not cause violations of any other constraints.
• Separate reserve requirements can be defined for the whole system and for individual transmission-constrained zones, including overlapping zones. Multiple reserve categories and their local requirements can be co-optimized with energy and unit commitment.

• User-defined groups of generating units and demand can represent transmission-constrained zones or interfaces. Import limits, export limits or equality constraints can be imposed; reserve may be included where required. Zones may overlap.
• Generator contributions can be weighted using user-supplied coefficients, so that flows on individual circuits or interfaces can be represented as linear combinations of generation and demand. The coefficients can be Power Transfer Distribution Factors (PTDFs) or other sensitivity factors from a DC load-flow model, allowing pre-fault or post-fault flow limits to be applied. The coefficients are calculated outside Powerop and supplied as input data.

• Transmission Loss Multipliers (TLMs) can be specified for each generating unit and demand unit in each scheduling interval. The power generated or consumed is multiplied by the TLM to give the net power contracted to the market, so the schedule reflects the location of each unit.
• TLMs can be greater or less than one, and can vary through the day as the expected pattern of generation and demand changes.
• Losses on the interconnectors between demand regions can be represented by time-varying loss multipliers applied to the flow in each direction.
• Losses are represented by these user-supplied multipliers rather than calculated from a network model.

• Each generating unit has a 'no-load' or fixed operating cost and a number of incremental operating costs, which can define a non-linear profile of operating costs. These costs can alternatively be expressed as fixed and incremental heat rates multiplied by a fuel cost, in which case the fuel costs can vary over the study period. The incremental costs or heat rates modelled by Powerop do not have to be monotonically increasing with increasing power output.
• Each generating unit has either a single start-up cost, or a number of warmth-dependent start-up costs, corresponding to a number of warmth states (for example hot, warm and cold) of each generating unit, determined by the time that particular unit has previously been off-load. Powerop is able to take these into account in its optimization, even if start-up costs do not increase steadily with warmth state.
• Powerop is also able to model works power, which is the power taken from the electricity grid when running a particular generating unit or before a generating unit is synchronised with the grid. Like start-up costs, works power can be warmth-dependent, depending on the warmth condition of the generating unit when it starts-up, as determined by the time that unit has previously been off-load.
• Monetary start-up costs and start-up fuel requirements can be represented separately. Powerop accepts a hotter start-up cost exceeding a colder one, with a warning. Start-up heats must remain the same or increase as the warmth category becomes colder.
• User-specified costs can be applied to changes in output to represent ramping costs or discourage unnecessary changes in a unit's dispatch. Generation-dependent costs can also vary by unit and scheduling interval.

• Multiple sets of incremental prices or incremental heat rates, start-up costs or heats, and shut-down costs can be specified for each generating unit. Each set applies from a defined start time until the next set takes over.
• This suits markets where declared prices can change from day to day or within the day. Fuel prices can also be specified for each scheduling interval.

• Each generating unit can have minimum on and off times.
• There can be upper limits on the numbers of start-up events of each generating unit per day and also over longer periods.
• The user can specify inflexible running of generating units. This forces a generating unit to run over a specified time-period, with an output not less than a specified value. Alternatively, the output of a generating unit can be forced to be equal to a specified value over a specified time-period.
• Limits on start-ups can apply over the study period or over separate periods before and after a specified maintenance event.

• An Instruct On or Instruct Off command requests that a unit start up or shut down at a particular time, specified to the nearest minute. This is useful where the reason for running is contractual rather than economic.
• Instructions are intended to set a precise time for an otherwise reasonable event. If an instruction conflicts with another constraint, such as a minimum on or off time, warnings in the results identify the conflict.
• A conditional minimum output does not force a unit to run, but requires that, if it runs in the specified intervals, its output is at or above a given value. This can raise a unit's minimum output temporarily above its Minimum Stable Generation.
• Conditional minimum outputs are applied where they are consistent with the unit's run-up and run-down rates; they do not change start-up or shut-down times.

• When a generating unit is on, its power output must be at or above its Minimum Stable Generation value, except when the generating unit is starting up or shutting down.
• The output of a generating unit must not exceed a specified maximum value.
• In Powerop's input data, the user can specify capacity restrictions on generating units. These reduce the maximum outputs of the units over specified time-periods. Alternatively, the user can specify different values of the available capacities of generating units in different time-periods ('profiling the availabilities').
• For each generating unit and reserve category, reserve capability can be represented by a piecewise-linear profile of reserve against power output. This allows detailed representation of how the reserve available from a unit changes with its dispatch level. Multiple categories can be co-optimized against system-wide and zonal reserve requirements.
• Powerop can model warmth-dependent run-up profiles, with the initial warmth state determined by the time the unit has previously been off-load. Ramp rates can be specified over separate power-output ranges, with independent input values for each warmth state, such as hot, warm and cold. Once sufficient time has elapsed after start-up for the unit to reach Minimum Stable Generation (MSG), Powerop treats it as hot; the other warmth-state ramp-rate profiles therefore apply during start-up. Run-down rates, which are not warmth-dependent, apply when a unit shuts down.
• Generating units are also subject to maximum loading and deloading rates, which apply when running continuously above Minimum Stable Generation.
• The user can specify a Synchronizing Generation Level below Minimum Stable Generation (MSG). Powerop can therefore account for usable power supplied to the grid during start-up before the unit reaches MSG.

• There can be 'station synchronizing intervals' and 'station desynchronising intervals' at some power stations. These are minimum time gaps between the start-ups and shut-downs of generating units at the same power station.

• Powerop optimizes charging and discharging of pumped-storage units and battery energy storage systems (BESS), allowing for losses in the storage and generating cycle.
• Pumped-storage reservoir levels can have upper, lower and target values, including end-of-study targets. Penalty costs determine how firmly applicable targets are enforced; reservoir inflows can also be represented.
• Minimum time delays between pumping and generating, and between generating and pumping, can be imposed on the same physical pumped-storage unit.
• BESS optimization co-optimizes charging, discharging, reserve and frequency response. See the BESS optimization page for details.

• Run-of-river hydro, which has no fuel cost and little choice over its output, can be modelled using a forecast output profile, in the same way as wind and solar generation.
• Reservoir hydro can be modelled as an energy-limited storage unit without pumping. Upper and lower reservoir limits, a daily inflow profile, and intermediate and end-of-study reservoir targets can be specified, all expressed in terms of stored electrical energy.
• Penalty costs on departures from reservoir targets determine how firmly they are enforced, so that water can be used earlier or later than planned where there is a sufficient economic case.
• Daily upper and lower energy limits on individual units can also represent restrictions on water use.
• Cascaded reservoirs, where releases from one reservoir affect inflows to another, can be represented approximately for short study periods; more detailed modelling can be discussed.

An electricity contract with another company can be modelled in Powerop as a pseudo-generating unit or as a 'demand' unit. This option offers all the features described above, for example ramp rates, energy limits, and minimum on and off times, which can be useful if the electricity contract contains such features.

Powerop also has an option for the direct modelling of the electricity contracts which are offered on power exchanges or by electricity brokers. Electricity contracts (also known as power contracts) may be of type 'buy' or 'sell'. Electricity contracts may optionally be grouped, so that if one contract in a group is accepted then all the other contracts in that group must be accepted. This direct modelling of electricity contracts allows Powerop to consider a much larger number of such contracts than if they were modelled as pseudo-generating units. This provides guidance to the electricity company as to which contracts to accept and at what volumes, in order to maximize profits, whilst taking into account the knock-on effects of accepting those contracts on the outputs of the physical generating units, including the effects of ramp rates.

• At the user's option, Powerop can either use specified initial conditions or choose its own. Using specified values of the initial conditions would be appropriate for short-term operational use. The option of letting Powerop choose its own initial conditions is suitable for longer-term planning purposes.

• Several demand regions can be modelled, each with its own demand and its own penalties for shortfall and rewards for surplus in each scheduling interval. Each generating unit and power contract is assigned to a region.
• The interconnector between each pair of regions can have different flow limits in each direction, which can vary by scheduling interval. Flows can also be held at or above a specified value, or fixed, in particular intervals.
• This suits regions that are organizationally distinct, for example where demand must be met in one region while a contracted position in another is followed with more modest penalties for departing from it.
• Imports and exports can be optimized, or Powerop can be used in a what-if mode to evaluate the costs or benefits of proposed transfers.
• Where more flexibility is needed, such as several interconnectors between the same regions, interconnectors can instead be represented using transmission constraint groups.

Powerop is able to use pseudo-generating units to model different modes of operation of generating plant, with logical constraints defined by the user preventing simultaneous operation of any incompatible modes. For example, the user might define different pseudo-generating units in Powerop's input data for the following modes of operation of a Combined Cycle Gas Turbine (CCGT) power station that has two gas turbines and one steam turbine: Mode A is Gas Turbine 1 operating in 'open-cycle mode', Mode B is Gas Turbine 2 operating in 'open-cycle mode', Mode C is Gas Turbine 1 operating in 'combined-cycle mode' with the Steam Turbine, Mode D is Gas Turbine 2 operating in 'combined-cycle mode' with the Steam Turbine, and Mode E is Gas Turbines 1 and 2 operating together in 'combined-cycle mode' with the Steam Turbine. The thermal efficiencies, ramp rates and other data items of these modes will not all be the same. This can be modelled by Powerop using pseudo-generating units and appropriate logical constraints specified by the user.

Logical constraints can also specify minimum or maximum numbers of operating units in a group, dependencies between units, and minimum scheduled synchronous inertia. Constraints can distinguish units producing any non-zero output from those at or above MSG.

Enhanced logical constraints can link operating decisions in consecutive scheduling intervals to restrict transitions between configurations, for example requiring an appropriate preceding operating mode before another mode can run.

When performing a planning study over a period of, say, one year, some considerations apply over all or most of the year, rather than at one particular time. Such issues include:

• Emission limits from various pollutants in fuels.
• Fuel-supply contracts (particularly for gas) and fuel constraints.
• Electricity contracts.
• Maximum numbers of start-ups for individual generating units (particularly for gas turbine plant, which commonly have a requirement for a maintenance overhaul after a certain number of start-ups).
• Scheduled overhauls and random breakdowns of generating units.
• Powerop's Planner option allows for scheduled overhauls by means of its input data and it can model random breakdowns probabilistically, including an optional variance-reduction capability.
• The above issues are dealt with by Powerop's Planner option optimizing the limited resources over the whole long-term study period, and then allocating them to subsequent shorter-term Powerop studies. Planner then collates the results of those Powerop studies together for user-friendly presentation to the user. All of the previously-described tasks are performed automatically by Planner. This means that long-term optimization is combined with a very accurate model of short-term generator scheduling. This allows longer-term studies to reflect short-term operating constraints in more detail than the simplified models often used for long-term planning purposes. This also means that there is no mismatch between short-term generator schedules and longer-term plans.

Running-hour limits can be applied to individual units or specified groups over defined periods, alongside emissions, energy and start-up restrictions. Planner allocates relevant longer-term resources across its linked short-term studies.

Powerop can account for the notice needed between issuing a start-up instruction and synchronizing a generating unit. Notification times can be specified by warmth state, separately from the subsequent run-up to MSG.

The schedule posting time and any previously instructed starts can be represented, so that a schedule takes account of which starts remain achievable. This option is particularly relevant to operational unit commitment and RUC.

Fuel procurement and use can be co-optimized with generation. Multiple buy, sell and buy-only contracts can supply one or more generating units, with restrictions on which contracts can supply which units.

• Total, daily and hourly limits, including take-or-pay terms.
• Daily block quantities and user-defined gas-day boundaries.
• Resale of unused gas from eligible buy contracts through available sell contracts.
• Transportation costs to individual generating units.

The same contract formulation can represent other fuels with similar supply restrictions.

Powerop distinguishes an actual reduction in available capacity from an operational restriction on scheduled output. A capacity derating affects both generation and upward reserve capability; an operational reduction can hold a unit below its available output while preserving its reserve capability.

Where a unit uses a piecewise-linear upward reserve profile, Powerop adjusts that profile when availability is reduced, following the profile rules described in the User Guide, which is available on request.

The Run Time Multiplier adjusts the solution effort to suit the required balance between run time and solution quality. The number of parallel threads used can also be selected.

An optional moving-horizon algorithm can be useful for some studies that are too large for an effective direct solution. It retains the problem data while progressively advancing the range over which decisions are made. Benefits depend on the data set; more threads or moving-horizon processing do not invariably shorten elapsed run time.

For longer planning studies, Planner solves overlapping shorter cases and allocates relevant resources across the full study period.

Results workbooks include detailed generation, reserve, storage, fuel and contract results, together with cost breakdowns. Transmission-group and logical-constraint results show their limits, calculated values, margins and any violation penalties.

Data-validation messages, warnings and errors are reported separately. This helps users assess a schedule and investigate conflicting data or requirements. Input data are retained with the study results.