{"id":1013659,"date":"2026-04-21T13:21:58","date_gmt":"2026-04-21T16:21:58","guid":{"rendered":"https:\/\/www.psr-inc.com\/analytics-report\/post\/economic-impact-assessment-of-hydraulic-operating-constraints-in-brazilian-hydropower-plants\/"},"modified":"2026-04-23T12:11:16","modified_gmt":"2026-04-23T15:11:16","slug":"economic-impact-assessment-of-hydraulic-operating-constraints-in-brazilian-hydropower-plants","status":"publish","type":"analytics_post","link":"https:\/\/www.psr-inc.com\/en\/analytics-report\/post\/economic-impact-assessment-of-hydraulic-operating-constraints-in-brazilian-hydropower-plants\/","title":{"rendered":"Economic impact assessment of hydraulic operating constraints in brazilian hydropower plants"},"content":{"rendered":"<div class=\"vgblk-rw-wrapper limit-wrapper\">\n<h2 class=\"wp-block-heading\"><h2><span lang=\"EN-US\">Context<\/span><\/h2><\/h2>\n\n<p>The Brazilian power matrix is predominantly composed of hydroelectric plants, which are responsible for most of the country\u2019s electricity generation. The National Electric System Operator (ONS) coordinates and controls the operation of the National Interconnected System (SIN).<\/p>\n\n<p>To ensure safe and economic operation, it is essential to consider the operational limitations of these plants, both in real-time operation and in the representation of constraints within electrical-energy models for planning and operation scheduling.<\/p>\n\n<p>Among the examples of hydroelectric plant variables subject to Hydraulic Operating Constraints (<strong>COPHIs, from the Portuguese acronym<\/strong>) are water levels (upstream and downstream) and flow rates (outflow, turbined, and spilled), for which minimum and\/or maximum value limits may be established, as well as rates of increase or decrease. COPHIs are necessary to ensure:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Multiple uses of water, compliance with federal and state regulations, and adherence to demands related to socio-environmental issues.<\/li>\n\n\n\n<li>Adherence to the operating guidelines of each reservoir, which are declared to promote better management of SIN\u2019s hydro-energy resources, including definitions from the National Water and Sanitation Agency (ANA) resolutions.<\/li>\n\n\n\n<li>Safety in the execution of activities and services that require the control of hydraulic variables at the sites.<\/li>\n\n\n\n<li>The performance of interventions on plant structures (spillways, powerhouses, etc.) that result in some type of restriction on hydraulic variables.<\/li>\n<\/ul>\n\n<p>The occurrences of the main upstream and downstream hydraulic operating constraints registered and in effect were observed in the ONS \u201cHydraulic Restriction Update Request Form\u201d (FSARH acronym in Portuguese) extract conducted on November 28, 2024.<\/p>\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td>Maximum Water Elevation<\/td><td>Minimum Water Elevation<\/td><td>Water Elevation decrease rate<\/td><td>Water Elevation increase rate<\/td><\/tr><tr><td>Upstream  Constraints<\/td><td>50%<\/td><td>40%<\/td><td>6%<\/td><td>3%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td>Max. Water Outflow<\/td><td>Min. Water Outflow<\/td><td><strong>Outflow increase rate<\/strong><\/td><td><strong>Outflow decrease rate<\/strong><\/td><td><strong>Ecological minimum Flow<\/strong><\/td><td><strong>Other<\/strong><\/td><\/tr><tr><td>Downstream Constraints<\/td><td>31%<\/td><td>33%<\/td><td>17%<\/td><td>8%<\/td><td>6%<\/td><td>5%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<p>COPHIs are established in Submodule 4.7 of the ONS Grid Procedures (&#8220;Inclusion and updating of hydraulic operating constraints for hydroelectric projects&#8221;). They are highly relevant to the planning, scheduling, and real-time operation processes, given their implications for hydroelectric plant operation.<\/p>\n\n<p>Recent years have seen a significant increase in COPHIs registered with the ONS to ensure the multiple uses of water, compliance with federal and state regulations, and adherence to socio-environmental and energy-related demands. In aggregate, these COPHIs reduce the <strong>operational flexibility<\/strong> of hydroelectric plants, which has economic implications for system operation. For example, it may be necessary to dispatch a gas-fired thermal plant to compensate for short-term variations in renewable sources or to keep it synchronized during periods when hydroelectric plants are constrained.<\/p>\n\n<p>In 2025, the ONS contracted <strong>PSR<\/strong>, seeking improvements in the analysis, evaluation, acceptance, and management process of the COPHI constraints that comprise the SIN. One of the activities developed by PSR in this project was a methodology to assess the <strong>economic impact<\/strong> of each COPHI without judging its necessity. However, by applying the methodology, it is possible to establish a ranking of which COPHIs are the costliest. The expectation is that this exercise will guide more objective discussions on whether it is possible to act to reduce or make these COPHIs more flexible.<\/p>\n\n<p>A little over a decade ago, during a prolonged drought that particularly affected the S\u00e3o Francisco River, it became clear that it was possible to reduce the downstream flow of some plants, such as the Tr\u00eas Marias HPP. This flow was being used to maintain a specific level required for urban water supply intake. The installation of floating pumps\u2014a simple project with a relatively small budget\u2014allowed for a reduction in these flows, preserving water in the plant&#8217;s reservoir to prevent it from emptying. This measure provided a benefit far exceeding the cost of the adaptation.<\/p>\n\n<p>The ranking of the highest-impact COPHIs can help guide where to look first. The Tr\u00eas Marias case illustrates how targeted interventions outside the traditional power-sector scope may yield benefits far greater than their cost, even if there is no guarantee that such success will always be replicated.<\/p>\n\n<p>This article discusses the project component where PSR develops a methodology for evaluating COPHIs by considering their consequences and repercussions under various operating conditions of the National Interconnected System (SIN). The methodology is grounded in the concept of marginal costs.<\/p>\n\n<h2 class=\"wp-block-heading\">Marginal costs<\/h2>\n\n<p>Marginal costs represent the rate of change of the objective function with respect to infinitesimal variations in the independent terms of the constraints (i.e., the Right-Hand Side or RHS of the constraints). In practical terms, they indicate how much the optimal value of the objective function (e.g., total cost or profit) changes when the availability of a resource (such as generation capacity, flow constraints, etc.) is slightly increased or decreased.<\/p>\n\n<p>In the context of power system optimization problems, marginal costs are widely used to evaluate the impact of constraints. In a hydroelectric plant dispatch problem, for example, the marginal cost of a minimum flow constraint indicates how much the total system cost would increase if the minimum flow were raised. Constraints with high marginal costs are identified as critical to the system and relaxing them can bring significant benefits. Thus, marginal costs are useful for identifying bottlenecks and can guide decisions on where to expand plant capacity or transmission lines.<\/p>\n\n<p>The marginal cost of the <em>k<sup>th<\/sup><\/em> COPHI, at a stage \u00a0and a hydrological scenario , expressed by , measures the variation in the SIN operating cost (in R$) resulting from a change in the COPHI. Some illustrative examples are presented next.<\/p>\n\n<h2 class=\"wp-block-heading\"><p class=\"Subtitulo2\"><span lang=\"EN-US\">Impact Assessment of Permanent COPHIs<\/span><\/p><\/h2>\n\n<p>There are currently about 600 active COPHIs, the majority of which are permanent constraints. One possible approach is to evaluate the impact of each COPHI separately by deactivating them one by one. To do this, the difference \u00a0is calculated between the operating cost of the SIN simulation considering all active COPHIs (base case) and the cost of the simulation when the <sup>th<\/sup> COPHI is removed.<\/p>\n\n<p>If the operating cost of the SIN with all COPHIs &#8220;deactivated&#8221; is , and if \u00a0is the base case cost includes all COPHIs, then the economic impact of the <sup>th<\/sup> COPHI is calculated as:<\/p>\n\n<p>$$\\text{Impacto}<em>k = (Z &#8211; Z_0) \\frac{\\Delta Z_k}{\\sum<\/em>{j=1}^{K} \\Delta Z_j}$$<\/p>\n\n<p>This cost allocation method, known as &#8220;last-addition&#8221;, or the &#8220;first-addition&#8221; alternative (which measures the difference between the SIN [National Interconnected System] cost when only the <sup>th<\/sup> COPHI is activated and the cost when no COPHI is active), has the disadvantage of requiring \u00a0simulations, which is computationally expensive. Furthermore, it covers only one of the many possible combinations of COPHIs that may be activated or deactivated. Thus, strictly speaking, it would be necessary to evaluate the contribution of each COPHI for all possible combinations of which ones are active.<\/p>\n\n<p>The Shapley Allocation Method, in turn, is used to distribute costs or benefits among participants fairly, considering each one&#8217;s contribution to the total result. This method is applied in various fields, such as economics, data science, and resource management. Its basic concepts involve:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Cooperative Game: when agents cooperate to obtain a collective benefit.<\/li>\n\n\n\n<li>Characteristic Function (<strong><em>v<\/em><\/strong>): represents the value for any subset of players. For a set of players,\u00a0<em>v(S)<\/em>\u00a0is the value.<\/li>\n\n\n\n<li>Marginal Contribution: the difference a player <em>i <\/em>(COPHI) adds when entering a group <em>S<\/em>, expressed by: <em>v(S \u222a {i}) \u2212 v(S)<\/em><\/li>\n<\/ul>\n\n<p>For a player <em>i<\/em>, the Shapley value \u03c6\u1d62\u00a0is given by:<\/p>\n\n<p>$$\\phi_i = \\sum_{S \\subseteq N \\setminus {i}} \\frac{|S|!(n &#8211; |S| &#8211; 1)!}{n!} [v(S \\cup {i}) &#8211; v(S)]$$<\/p>\n\n<p>Where:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><em>N<\/em> corresponds to the set of all players.<\/li>\n\n\n\n<li><em>S<\/em> is a subset of participants excluding <em>i<\/em>.<\/li>\n\n\n\n<li><em>v(S)<\/em> is the value (or cost) of coalition <em>S<\/em>.<\/li>\n\n\n\n<li><em>v(S \u222a {i})<\/em> is the value of the coalition formed by adding player <em>i<\/em> to <em>S<\/em>.<\/li>\n\n\n\n<li>|<em>S<\/em>| corresponds to the size of subset <em>S<\/em>.<\/li>\n\n\n\n<li>The fraction |<em>S<\/em>|!(<em>n<\/em> \u2212 |<em>S<\/em>| \u2212 1)! \/ <em>n<\/em>! is the weight given to all formation orders of the groups.<\/li>\n<\/ul>\n\n<p>For example, take three power plants (A, B, C) generating energy. The Shapley method calculates how much each plant should be compensated based on its contribution to all possible combinations (A alone, A+B, A+C, A+B+C, etc.).<\/p>\n\n<p>The practical limitation of applying this method is the number of combinations that would need to be tested for a set of hundreds of COPHIs. To bypass this issue, an extension of this method is suggested: the Aumann-Shapley (A-S) method, usually used in situations where factors are not discrete, but continuous. It is especially useful for cost allocation problems in systems such as power grids or supply chains.<\/p>\n\n<p>While the Shapley method deals with individual elements, the Aumann-Shapley method deals with continuous fractions of contribution. Instead of allocating costs to individual plants, it allocates costs to shared resources. Its operation is related to:<\/p>\n\n<ol class=\"wp-block-list\">\n<li>Integration of Marginal Contributions: The method calculates the marginal contribution of each continuous fraction (e.g., each MWh generated) and integrates these contributions along a continuous path.<\/li>\n\n\n\n<li>Integration Path: The allocation is made along a path from &#8220;no contribution&#8221; to &#8220;total contribution.&#8221;<\/li>\n<\/ol>\n\n<p>For a continuous resource , the allocated cost \u03c6(<em>x<\/em>) is given by:<\/p>\n\n<p>$$\\Phi(x) = \\int_0^1 \\frac{\\partial C(t \\cdot x)}{\\partial x} dt$$<\/p>\n\n<p>Where:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><em>C(t \u00b7 x)<\/em> is the total cost when resource <em>x<\/em>\u00a0is scaled by a factor <em>t<\/em>.<\/li>\n\n\n\n<li>\u2202<em>C(t \u00b7 x)<\/em> \/ \u2202<em>x<\/em> is the marginal cost of resource <em>x<\/em> at point <em>t \u00b7 x<\/em>.<\/li>\n<\/ul>\n\n<p>It is suggested to apply the A-S method to evaluate the impact of COPHIs:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>The system operating cost is the function to be allocated.<\/li>\n\n\n\n<li>The objective is to quantify the marginal impact of each COPHI on the total operating cost of the SIN, considering their interdependence.<\/li>\n<\/ul>\n\n<p>O m\u00e9todo A-S permite uma aloca\u00e7\u00e3o justa e eficiente. Em sua vers\u00e3o discreta por fra\u00e7\u00e3o, todas as COPHIs s\u00e3o escaladas conjuntamente de seu valor atual para um valor m\u00ednimo (para restri\u00e7\u00f5es de limite inferior \u2014 &#8220;maior ou igual a&#8221;) ou um valor m\u00e1ximo (para restri\u00e7\u00f5es de limite superior \u2014 &#8220;menor ou igual a&#8221;). Passos discretos s\u00e3o usados para medir o incremento de custo do SIN. O impacto marginal de cada restri\u00e7\u00e3o \u00e9 obtido por meio dos custos marginais. O algoritmo A-S possui quatro etapas principais:<\/p>\n\n<p><strong>Step 1:<\/strong> Define discrete steps<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Choose <em>N<\/em> (e.g., <em>N<\/em> = 10).<\/li>\n\n\n\n<li>Define <em>a\u2099 = n\/N<\/em>, where <em>n<\/em> = 0, &#8230;, <em>N<\/em>, to scale all types of COPHIs, for all plants, stages, and hydrological scenarios. The increment is 1\/<em>N<\/em>.\n<ul class=\"wp-block-list\">\n<li>When <em>a\u2099<\/em> = 1, a COPHI <em>k<\/em> receives its original value.<\/li>\n\n\n\n<li>When <em>a\u2099<\/em> = 0, the COPHI <em>k<\/em> is relaxed.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n<p><strong>Example of operating flow limits for COPHI k:<\/strong><\/p>\n\n<p>The premises for the minimum and maximum constraint values must be carefully defined. The relaxed minimum flow could be, for example, the historical minimum daily flow of a plant, while the relaxed maximum flow can be the value used in calculating the flood control volume. This definition is crucial for a more realistic assessment of the economic impact of COPHIs.<\/p>\n\n<p><strong>Step 2:<\/strong> Simulate the system with tiered constraints<\/p>\n\n<p>For each step <em>n = <\/em>0<em>, &#8230;, N<\/em>:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Scale all COPHIs for all plants by the factor <em>\u03b1\u2099<\/em>.<\/li>\n\n\n\n<li>Solve the SIN (National Interconnected System) optimization problem using <strong>SDDP<\/strong> for medium-term operation planning for these COPHIs that includes different generation technologies (hydropower plants, thermal power, solar power, wind power), the transmission network and storage technologies, such as pumped hydro and battery.<\/li>\n\n\n\n<li>\u2022 Calculate the system operating cost <em>z\u2099<\/em> and the incremental cost \u0394<em>z\u2099<\/em> = <em>z\u2099 \u2212 z\u2099\u208b\u2081<\/em>.<\/li>\n<\/ul>\n\n<p><strong>Step 3:<\/strong> Calculate the individual contribution of each COPHI \u00a0to the cost increment.<\/p>\n\n<p>Seja \u0394<em>k,n<\/em> = \u03a3<em>\u209c\u03a3\u209b<\/em> (\u03c0<em>k,t,s<\/em>\u207f \u00b7 COPHI<em>k<\/em>\u207f \u2212 \u03c0<em>k,t,s<\/em>\u207f\u207b\u00b9 \u00b7 COPHI<em>k<\/em>\u207f\u207b\u00b9) e \u0394I<em>k,n<\/em> = \u0394<em>z\u2099<\/em> \u00b7 \u0394<em>k,n<\/em> \/ \u03a3<em>\u2096<\/em>\u0394<em>k,n<\/em><\/p>\n\n<p>O impacto econ\u00f4mico final de cada COPHI <em>k<\/em> \u00e9 I<em>k<\/em> = \u03a3\u2099 \u0394I<em>k,n<\/em><\/p>\n\n<p><strong>Etapa 4:<\/strong> Gerar o Ranking das Restri\u00e7\u00f5es<\/p>\n\n<p>Ordenar as COPHIs por seus impactos em valores absolutos. O ranking deve fornecer uma desagrega\u00e7\u00e3o por tipo de COPHI (e.g., vaz\u00e3o m\u00ednima, vaz\u00e3o m\u00e1xima, etc.) e por UHE (Usina Hidrel\u00e9trica).<\/p>\n\n<h2 class=\"wp-block-heading\"><strong>Exerc\u00edcio Num\u00e9rico<\/strong><\/h2>\n\n<p>O procedimento Aumann-Shapley (A-S) para avalia\u00e7\u00e3o do impacto econ\u00f4mico das COPHIs foi inicialmente testado com o SDDP, devido \u00e0 facilidade de implementa\u00e7\u00e3o pela PSR e \u00e0 disponibilidade dos componentes necess\u00e1rios (modelagem de restri\u00e7\u00f5es e prepara\u00e7\u00e3o dos custos marginais associados). As seguintes premissas foram utilizadas:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Deck do PMO de maio de 2025 (Programa Mensal de Opera\u00e7\u00e3o), com horizonte de 5 anos e 200 cen\u00e1rios sint\u00e9ticos de aflu\u00eancias gerados pelo modelo PAR(p) do SDDP, sem uso de reamostragem de cen\u00e1rios na pol\u00edtica.<\/li>\n\n\n\n<li>O A-S foi implementado em c\u00f3digo Julia para alocar impactos econ\u00f4micos usando 10 pontos (0%, 10%, 20%, &#8230;, 100%), ou seja, 11 rodadas do SDDP com c\u00e1lculo de pol\u00edtica e simula\u00e7\u00e3o para cada caso utilizando os 200 cen\u00e1rios de hidrologia.<\/li>\n\n\n\n<li>Foram adotados valores para cada COPHI, conforme apresentado na Tabela 1.<\/li>\n<\/ul>\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>COPHI<\/strong><\/td><td><strong>Premissa para o caso 100% relaxado<\/strong><\/td><\/tr><tr><td>Vaz\u00e3o m\u00ednima<\/td><td>Vaz\u00e3o total m\u00ednima di\u00e1ria hist\u00f3rica<\/td><\/tr><tr><td>Vaz\u00e3o m\u00e1xima<\/td><td>Vaz\u00e3o utilizada no c\u00e1lculo do volume de controle de cheias, se aplic\u00e1vel. Caso contr\u00e1rio, o m\u00ednimo entre 2x a vaz\u00e3o m\u00e1xima e a vaz\u00e3o de projeto do vertedouro.<\/td><\/tr><tr><td>Volume m\u00edn.\/m\u00e1x.<\/td><td>Volume operativo m\u00ednimo\/m\u00e1ximo<\/td><\/tr><tr><td>Rampas m\u00edn.\/m\u00e1x. de n\u00edvel<\/td><td>Zero<\/td><\/tr><tr><td>Rampas m\u00edn.\/m\u00e1x. de vaz\u00e3o<\/td><td>Zero<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<p class=\"has-text-align-center\"><strong>Tabela 1 \u2013 Premissas para os valores das COPHIs no caso relaxado<\/strong><\/p>\n\n<p class=\"has-text-align-left\">.<\/p>\n\n<h2 class=\"wp-block-heading\"><h2><a name=\"_Toc222737225\"><\/a><span lang=\"EN-US\">Results<\/span><\/h2><\/h2>\n\n<p>The proposed procedure was executed, consisting of 11 SDDP runs (for points ranging from 0% to 100% in 10% steps), the May 2025 PMO deck with a 5-year horizon, 200 hydrological scenarios, and the inclusion (for now) of the previously presented COPHIs:<\/p>\n\n<ol class=\"wp-block-list\">\n<li>Minimum outflows<\/li>\n\n\n\n<li>Minimum water level per plant<\/li>\n\n\n\n<li>Minimum turbined flow<\/li>\n\n\n\n<li>Minimum spilled flow<\/li>\n<\/ol>\n\n<p>The following figure presents the allocation of the increase in operating costs in the 5-year horizon in proportion to the product of the marginal cost and the COPHI increment for the 15 plants with the highest values obtained using this methodology.<\/p>\n\n<p>Among the results presented, it is interesting to highlight the costs of the following plants:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>Pimental:<\/strong> presents the highest value (not shown to avoid distorting the others), as it represents the effect of the outflow required to meet the hydrograph; this water is no longer turbined at Belo Monte (a plant with ~90 m of head) and is instead turbined at Pimental (~20 m of head) or spilled. The average impact is R$ 850 million. The following figure shows the economic impact value for each simulated scenario. This helps understand the <em>dispersion<\/em> of the economic impact results. The Pimental site, for instance, has a minimum environmental flow rate (monthly values) that must be kept in the Xingu River, thus does not generate electricity. Depending on the SIN supply condition, this energy loss can have more limited or more severe effects.<\/li>\n\n\n\n<li><strong>Porto Primavera and Xing\u00f3<\/strong>: consider their structural minimum outflow values, which are 4,600 m\u00b3\/s and 800 m\u00b3\/s, respectively.<\/li>\n\n\n\n<li><strong>Jupi\u00e1<\/strong>: reflects minimum flow constraints.<\/li>\n\n\n\n<li><strong>Barra Bonita, Tr\u00eas Irm\u00e3os, and Ilha Solteira<\/strong>: reflect minimum level constraints for the maintenance of the Tiet\u00ea-Paran\u00e1 waterway operations.<\/li>\n\n\n\n<li><strong>Sobradinho and Itaparica<\/strong>: reflect minimum flow constraints and the impact of operating guidelines.<\/li>\n\n\n\n<li><strong>Funil (Para\u00edba do Sul)<\/strong>: reflects the minimum storage level of 30% defined in Joint Resolution ANA\/INEA\/DAE\/IGAM No. 1382\/2015.<\/li>\n\n\n\n<li><strong>Machadinho<\/strong>: reflects the minimum turbined flow conditioned on the reservoir level, as stated in FSAR-H 2858\/2022, which defines a minimum flow of 295 m\u00b3\/s when the level is above 469.9 m and 250 m\u00b3\/s when below that elevation.<\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">Evaluation Criteria and Cost-Benefit Analysis<\/h2>\n\n<p>The ranking of COPHIs by their economic impact, derived from the Aumann-Shapley method, serves as a critical strategic guide. It does more than identify constraints: it helps indicate where the largest potential savings may lie.<\/p>\n\n<p>While the ranking identifies the costliest constraints, it is important to recognize that &#8220;relaxing&#8221; a COPHI is not always feasible or appropriate. In practice, the scope for flexibility tends to fall into two categories:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>Inflexible Constraints<\/strong>: Some COPHIs are essential for non-negotiable socio-environmental protections. For example, a minimum flow requirement may be vital to ensure the survival of an endangered and endemic species in a specific river reach. In such cases, the &#8220;cost&#8221; to the ecosystem of relaxing the constraint would be infinite, making flexibility impossible regardless of the economic impact on the power sector.<\/li>\n\n\n\n<li><strong>Flexible Constraints via Investment:<\/strong> Many constraints exist to protect specific human activities that can be adapted with targeted investment. A primary example is the Tr\u00eas Marias HPP, where downstream flow was maintained at a high level solely for urban water supply intake. By investing in floating pumps &#8211; a simple, low-budget project &#8211; the system allowed for reduced flows and preserved reservoir levels, yielding a benefit that far exceeded the adaptation cost.<\/li>\n<\/ul>\n\n<p>To move from a theoretical ranking to practical action, a marginal cost-benefit analysis is required. This involves comparing two distinct curves:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>The Benefit Curve:<\/strong> Represents the cumulative reduction in SIN operating costs as a COPHI is incrementally relaxed. This is calculated by simulating the system (e.g., using the SDDP model) under varying constraint limits.<\/li>\n\n\n\n<li><strong>The Cost Curve:<\/strong> Represents the investment required (structural or non-structural) to enable that flexibility.<\/li>\n<\/ul>\n\n<p>ADICIONAR FIGURA<\/p>\n\n<p>Figure 2 \u2013 Cost-benefit curve graph<\/p>\n\n<p>The optimal degree of flexibility is found at the intersection of these two curves. By applying this logic, COPHIs can be prioritized to guide decision-making considering low-to-moderate cost solutions with rapid or medium-term implementation that provide significant gains. On the other hand, complex structural interventions with high costs and long timeframes may only be justified if the benefit to the SIN is substantial.<\/p>\n\n<p>This analytical approach is directly relevant to electricity consumers, since any reduction in SIN operating costs ultimately translates into lower tariffs. A significant regulatory hurdle remains, however: the power sector often lacks the legal mandate to fund or execute adaptations outside its immediate domain, even when those interventions would clearly reduce system costs. The Tr\u00eas Marias case illustrates this point well: the installation of floating pumps for urban water intake made it possible to reduce downstream flow requirements, preserve reservoir storage, and generate benefits far greater than the cost of the adaptation. A proactive inter-institutional framework would help enable similar cross-sector investments whenever the benefit to electricity consumers exceeds the cost of the required external intervention.<\/p>\n<\/div><!-- .vgblk-rw-wrapper -->","protected":false},"featured_media":1013533,"template":"","meta":{"_acf_changed":false},"report_section":[480],"class_list":["post-1013659","analytics_post","type-analytics_post","status-publish","has-post-thumbnail","hentry","report_section-indepth"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Economic impact assessment of hydraulic operating constraints in brazilian hydropower plants - PSR Energy<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.psr-inc.com\/en\/analytics-report\/post\/economic-impact-assessment-of-hydraulic-operating-constraints-in-brazilian-hydropower-plants\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Economic impact assessment of hydraulic operating constraints in brazilian hydropower plants - PSR Energy\" \/>\n<meta property=\"og:description\" content=\"Context The Brazilian power matrix is predominantly composed of hydroelectric plants, which are responsible for most of the country\u2019s electricity generation. 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