Energetic planning and operative constraints: How does DESSEM works?

The following white paper demonstrate power generation programming suggested by DESSEM violated Hydropower Plants operative regulatory constraints about 70% of times.

Brazilian National Regulator Institution to the Electric System is ONS (Operador Nacional do Sistema Elétrico), responsible for coordinating and controlling Electric Power generation and transmission of the whole system.

In January 2020, ONS implemented DESSEM, a short term optimizer hydropower dispatch model. The main purpose of this model is to promote efficiency and informational symmetry among companies operating in the Electric Sector. This model established parameters to generation and to   operation, in order to promote harmony between plant, environment and communities located around it.

An analyses of available DESSEM data shows the about 70% of daily Electric Power generation programming in Brazilian plants violated operational constraints previously established. Of all analyzed cascades, only one did not show any type of violation. This fact is related to the simplicity of this specific cascade, with only one plant.

The main constrains violations are related to operation of hydraulic generation units operative range, around 28 notified cases from 40. The following table summarizes the number of violated restriction to each analyzed cascade:

Number of Constraints Restrictions
Operative range Violation
Grande CS
Grande TR
São Francisco

In this file we present the results of a one week long use of HydroPower Optimizer considering DESSEM data:

Collect generation programm proposed by DESSEM
Hydropower Optimizer simulation considering generation settings suggested by Dessem’s program.
Result analyses such as: operative constraints violation, generation violating established parameters Variation Constraint, etc.

Time-Lapse Analysis

Considering semi-hourly programming of a selected power plant, totalizing 48 generation parameters.  The comparison results were classified by cascades, for example, Jequitinhonha, where only one power plant is located, with at least 48 operational restriction to this plant. In the other hand, Grande CS’ cascade, with 3 power plants, counts up to 144 restrictions.

The following graphic shows the results for all those cascade, considering observed violation percentage:

The data bellow show that concerning around 70% of scheduled generation periods the operational or environmental constraints were violated. We can also verify that only one cascade, Itabapoana, did operate violating not a single constraint. This specific case, though, is characterized for being a the only power plant operating on that river (Rosal) and this, therefore, make it easier to operate.

Cascade Analysis

Following we present the types of operational constraint detailed by each power plant in a 7 days period:

Due to differences between the cascades, we observed that there was not a pattern concerning constraints. At ‘Doce’ power plant, for instance, environmental constraints occurred more often. In the other hand, when considering ‘Grande CS’ power plant, parameter variation were the predominant observed constraints.

Following we show information concerning systematic constraints violation by day of the week

Cascade Constraints Analysis

A similar comparison was made to every other cascade Hydropower Optimizer has access to operative data. Those are: Araguari, Doce, Grande CS, Grande TR, Itabopoana, Jquitinhonha, Paranaíba e São Francisco. The graphic considered the sum up of violated constrants, classified by type.

HydroPower Optimizer

Considering Itutinga power plant’s DESSEM generation programming, by simulating generation demand: (1) without disabling operative constraints corrections and (2) e enabling these corrections.

The graphics presents the simulation for  DESSEM’s designated  programming parameters for Itutinga Hydropower Plant caused operative Parameters Variation Constraint at 9am. In the other hand, by enabling HydroPower Optimizer corrections, the simulation suggests that production of each generation unit occurs according to operational parameters, in attendance to it limitations.

Following the definition of some concepts used on this whitepaper:

The simulations hereby analyzed are based on hydropower cascade concept, in other words, power plants located in the same river basin. Thereby, power plant operation upriver impact reservoirs of power plants downriver, consequently, interfering in water flow downriver the cascade to other powerplants. The follow image illustrate the concept:


Graphic representation of power plants cascade dispositions on Rio Doce’s basin.

The water balance is an important natural constraints, mandatory to be considered into the hydropower generation models. It consists, basically, in the fact that Power plant reservoir’s final volume needs to equal to initial volume added together to gains (natural water flow arriving to plant’s reservoirs) deducted losses (dispatched flow and evaporation, for example).

Hydropower Optimizer

HPO is a computational system to daily hydropower generation dispatch considering multi objectives. It works as a simulator, by measuring reference parameters (power or turbine water flow), responsible for simulating the power plant operation. Among these characteristics, it highlights violation of restrictions, promoting corrections to promote permanent satisfactory operational parameters, attendance to regulatory constraints; optimize use performance of each single generation unit solving the unit commitment problem, while considering scheduled maintenance.

The main advantage in relation to DESSEM’s model is the detailed modeling considering each generation unit in the plant. It also considers yield function (Hill Curve) of units making the dispatch optimized and more adherent to the real life operation of the plant. Attendance to electrical, physical and operational constraints of the plant and supports the best use of energetic resources, while reducing operational costs.

Other advantages are computational time saving while making and utilizing the model in a more practical way. This is due the fact that the system offers power plant data base integration and water flow forecast, allowing also real time continuous checking of power plant parameters.


Power plant generation parameters are directly linked to several different operational variables such as, internal ones, power plant’s maximal storage or/and, external ones, environmental constraints. Those are limitations to maximal efficiency and are considerate in order to mitigate environmental impacts. Following we present the operative regulation concern Power Plants.

Maximum storage: It defines superior working volume limit of a reservoirs

Minimum storage: minimum normal operational level. It defines dead volume level of a reservoirs, in other words, the volume that is not used for power generation.

Spilled flow rate: concerning some spillways, maximum water flow limits are required in order to avoid damaging this physical structures.

Generation variation rate: this constraints relates to spilled water flow, implicating in a variation power rate limitation generation for the power plant. In Itutinga, for example, in some periods of the year, the reduction in power generation can reach 7MW for each 30 minutes due to this constraints.

Daily water flow variation rate: this type of constraints relates to reservoir’s daily maximum decrease level.

Maximum Outflow Rate: violating this restriction may incur on flooding roads and cities nearby the river or destroying bridges. Considering Camargo’s Power Plant, for instance, any giver value above 1000m³/s may results on flooding access road to Ituting’s Power Plant.

Minimum Outflow Rate: this parameter defines minimum water flow rates to be reached in order to avoid damage to the watercourse and hydrographic basin, considering other variables such as water catchment downriver.

Piracema Period: reproductive period, when fishes move towards river’s source to spawn. On this period some power plants are required minimum outflow to avoid trap fishes. At Nova Ponte’s Power plant, for example, minimum spill out is 80 m³/s to avoid trapping fishes. 

Drought and Humid Period: On those periods, some power plants are demand to keep a minimum water flow downriver, a bigger value while on drought periods and a smaller value while on humid periods. At Queimado’s Power Plant, for example, on drought periods, the minimum water flow rate downriver is 17m³/s, meanwhile 8.8m³/s on humid periods. 

Night time and weekends: Regulatory norms demand to avoid opening spillways during night time.


Restrictive Range concerns operative limits of each generation unit (turbine).


The other constraints refers to the components used in each generation unit, such as turbines and generators, beside other intrinsic components to each power plant, such as water flow in the tunnel of a power plant with two reservoirs.

Following a image of the system, exemplifying registered constrains for the Power Plant of Igarapava.

Registered Constraints for Igaparava Power Plant on HPO


DESSEM (Modelo de Despacho Hidrotérmico de Curíssimo Prazo) is characterized for being a short term optimizer hydropower dispatch model, developed by CEPEL (Centro de Pesquisas de Energia Elétrica). The model development is a direct effort to promoting daily power generation programming as close as possible to real world conditions, and forecasting power productionselling prices at a given hourly period. It first started being used while programming daily power plant production on January of 2020. The main objective of this optimization model is determine daily program to hydrothermal, on a weekly base, on 30 minute periods during the day. Its usage is not linked to NEWAVE and DECOMP use, in other words, DESSEM adapts itself to DECOMP’s future cost function in this study.


  • DC Electric grid modeling with or without losses
  • Safety grid constraints
  • Thermometric Power plan Unit Commitment constraints
  • Thermal Power Plant Operation
  • Power Plant Productivity according to height of water fall
  • Water Balance considering water travel time to plant
  • Pumping Power plant and canals among reservoirs
  • Intermittent sources (wind and solar)

The challenge of daily planning operation, a very complex problem considering a huge system such as Brazilian grid, demands a model to determine hourly dispatch to following day (programming stage). There are many problems that induce a spacial interaction (coordination among the different power plants and the consideration of power plants geographically positioned in cascade) and temporal (water travel time and decision between water catchment or storage).

Current Perspective for the National Integrated System and Programming Challenges.

In order to deliver the most realist model, the system will consider the electric transmission grid into the DC modeling, aiming to limit the flow in the circuits and to diminish transmission losses, either during energy exchange or among the subsystems.

DESSEM’s model always consider optimization, allowing the programming to reach expected value for uncertain variables, using as a reference input data, resulting in a forecast model.