{"id":3138,"date":"2026-02-26T22:33:06","date_gmt":"2026-02-26T22:33:06","guid":{"rendered":"https:\/\/renewasoft.com.tr\/?p=3138"},"modified":"2026-02-28T00:23:02","modified_gmt":"2026-02-28T00:23:02","slug":"digitalization-in-the-energy-market-less-risk-more-control-with-data-driven-decision-systems-hydrowise","status":"publish","type":"post","link":"https:\/\/renewasoft.com.tr\/index.php\/en\/2026\/02\/26\/digitalization-in-the-energy-market-less-risk-more-control-with-data-driven-decision-systems-hydrowise\/","title":{"rendered":"Digitalization in the Energy Market: Less Risk, More Control with Data-Driven Decision Systems (Hydrowise)"},"content":{"rendered":"

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RENEWASOFT | TECHNICAL BLOG<\/div>\n
Reading Time:<\/b> ~16-20 min \u00a0|\u00a0 Level:<\/b> Beginner \/ Intermediate
\nTarget Audience:<\/b> HES operators, SCADA engineers, energy company managers<\/div>\n<\/div>\n

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Meta description:<\/b> What does digitalization in the energy market really mean? We break it down with KPIs, a dashboard example, and a practical decision-support flow for hydro plants ending with the Hydrowise approach.<\/p>\n

Keywords:<\/b> digitalization in energy markets, data-driven decision system, decision support, HPP dashboard, KPI, SCADA integration, EP\u0130A\u015e data, imbalance management<\/p>\n<\/div>\n<\/div>\n

Digitalization in the Energy Market: Less Risk, More Control with Data-Driven Decision Systems (Hydrowise)<\/h1>\n

Hook + problem definition<\/h2>\n

In the energy market, the \u201cright decision\u201d rarely comes from a single number. Price, generation, water\/reservoir constraints, outage risk, market rules, and time all move at once. One team watches MCP\/PTF, another is busy with flow and gates on-site, and another monitors equipment signals\u2026 Everyone works hard, yet decisions still end up fragmented.<\/p>\n

This is exactly where digitalization becomes practical not as a vague \u201clet\u2019s automate everything\u201d slogan, but as a system that unifies data, turns it into meaning, and ties it to action. EP\u0130A\u015e\u2019s transparency approach and its data access channels (platform + web services) already strengthen the backbone of this transformation. [4][5][6] The real question is: how do you turn that into a decision system that balances revenue, risk, and operations on the hydro side?<\/p>\n

\n
TL;DR<\/div>\n
    \n
  • Digitalization is not collecting data it\u2019s producing decisions: SCADA + market + maintenance data must meet in one place and drive action.<\/li>\n
  • EP\u0130A\u015e transparency data makes the market measurable; web services make automation realistic.<\/li>\n
  • Dashboards\/KPIs are not \u201cjust reporting\u201d: they should answer \u201cwhere is deviation growing?\u201d on an hourly basis.<\/li>\n
  • Data-driven decision support manages forecast error + operational risk + market uncertainty together (not one metric, but a decision package).<\/li>\n
  • The Hydrowise approach combines real-time SCADA processing, forecasting, and BI\/reporting so HPPs can be managed from a single center.<\/li>\n<\/ul>\n<\/div>\n

    What does \u201cdigitalization\u201d mean in the energy market?<\/h2>\n

    In energy, digitalization typically becomes real in three layers:<\/p>\n

      \n
    • Visibility: Collect data, standardize it, make it reliable<\/li>\n
    • Insight (Forecast\/Insight): Extract signals forecasting, anomaly detection, risk scoring<\/li>\n
    • Action (Decision\/Automation): Translate insight into business rules and decision flows<\/li>\n<\/ul>\n

      IEA\u2019s perspective is similar: digital technologies can make energy systems more connected, efficient, and reliable, while also introducing new challenges such as cybersecurity and privacy. [8]<\/p>\n

      SHURA\u2019s T\u00fcrkiye-focused view highlights another angle: digital technologies create new business models and application areas in the energy transition; IoT, AI, and big data reshape both internal processes and value creation. [7]<\/p>\n

      What is a Decision Support System (DSS)?<\/h2>\n

      A decision support system accepts a simple truth: humans make the decision. The system\u2019s job is to bring the right indicators at the right time, in the right context, so decisions are faster and more consistent.<\/p>\n

      In energy markets, decision support is also used to monitor and score market behaviors so participants can adopt more data-driven bidding strategies. [12] The core idea is to institutionalize the pipeline: data \u2192 analysis \u2192 decision.<\/p>\n

      Why talk about data governance?<\/h2>\n

      Because decision support is only as good as the data behind it. If bad data enters the system, it can produce confident but wrong recommendations. On the ground, the most common issues are simple: the same tag stored under different names across systems, timestamps that don\u2019t align, alarms classified inconsistently\u2026<\/p>\n

      That\u2019s why the first step of digitalization isn\u2019t \u201cpulling data,\u201d but defining the data and assigning ownership: Who produces which data? How often does it update? In what format? Who validates it?<\/p>\n

      On the EP\u0130A\u015e side, published datasets and web-service access put market data on a more standard, repeatable footing. When a similar standardization is designed on the plant side, dashboards earn trust and decisions stop being debated every day. [4][6][15]<\/p>\n

      Why is it more critical now?<\/h2>\n
        \n
      • Data has exploded: SCADA is minute\/second-level; markets are hourly; maintenance is event-based.<\/li>\n
      • Uncertainty is higher: weather\/hydrology, demand shifts, transmission constraints, price volatility.<\/li>\n
      • Regulation and transparency are stronger: EP\u0130A\u015e Transparency Platform operates with a legal basis for publishing datasets. [4]<\/li>\n
      • In studies such as Competition Authority specialization theses, the transparency framework of the wholesale electricity market is examined in detail; the impact of data sharing on market performance and the debate around \u201ccritical data\u201d are emphasized. [9] This is why digitalization is not only about technology it\u2019s tightly linked to market design.<\/li>\n<\/ul>\n

        How does it work?<\/h2>\n

        The most practical way to build a digital decision system is not a \u201cone-shot mega project,\u201d but a phased flow.<\/p>\n

        Infographic<\/p>\n

        Figure-1<\/b><\/p>\n

        \"Figure-1
        Figure: Snapshot-style infographic summarizing why energy-market digitalization matters: fragmented data and slow decisions are replaced by a unified data layer, clearer KPIs, faster execution, and lower operational risk closing with a simple \u201crequest a demo\u201d call to action aligned with the Hydrowise approach.<\/figcaption><\/figure>\n

        Phase 1\u00a0\u00a0Data collection and unification<\/h3>\n
          \n
        • SCADA\/OT data: generation (kW\/MW), flow, gate position, reservoir level, vibration\/temperature, etc.<\/li>\n
        • Market data: MCP\/PTF, SMP\/SMF, volumes, announcements, transparency datasets<\/li>\n
        • Operational data: maintenance records, outages, asset health, shift notes<\/li>\n<\/ul>\n

          On the EP\u0130A\u015e side, data access isn\u2019t limited to manual Excel downloads; it can be automated via web services. And new service transitions and security updates (for example, changes in the way you obtain TGT tickets) are a reminder that integrations are living systems, not \u201cset and forget.\u201d [5][6]<\/p>\n

          \n
          Technical note: Why does it matter to pull EP\u0130A\u015e data automatically?<\/div>\n
          Manual downloads can cause delays exactly when it hurts the most during the busiest days and the most critical hours. Web service documents define JSON return types, date formats, and endpoint structures this is the foundation for a steady data stream into decision support. [6][10]<\/div>\n<\/div>\n

          Phase 2\u00a0\u00a0Quality control and interpretation<\/h3>\n

          Once data is unified, two questions must be answered:<\/p>\n

            \n
          • Is the data correct? (missing points, outliers, sensor drift)<\/li>\n
          • What is it telling us? (trend, anomaly, performance drop)<\/li>\n<\/ul>\n

            Reporting alone is not enough. Making SCADA \u201csmart\u201d through outlier detection, operating regime classification, and better loading strategies can be strengthened by data-mining-based modules. [11]<\/p>\n

            Phase 3\u00a0\u00a0KPI and decision rules<\/h3>\n

            This is where digitalization either becomes real or stays as \u201canother reporting project.\u201d KPIs must be selected not for monitoring, but for decision-making.<\/p>\n

            Example decision rule:<\/b>
            \n\u201cIf the gap between planned generation and realized generation exceeds band X \u2192 produce an intraday (G\u0130P) correction recommendation.\u201d
            \n(Data feeding the rule: SCADA + plan + market time)<\/p>\n

            Phase 4\u00a0\u00a0One-screen delivery (Dashboard)<\/h3>\n

            Renewasoft\u2019s reporting & BI approach emphasizes turning SCADA\/IoT data into meaningful insight so managers can track real-time analysis and trends. [2] The goal is not \u201cpretty charts\u201d it\u2019s one-glance decisions.<\/p>\n

            Impact on the HPP \/ energy facility side<\/h2>\n

            For a hydro plant operator, digitalization touches five very concrete pains:<\/p>\n

            1) Why do deviation meetings never end?<\/h3>\n

            Because the data lives in different places. The trading team sees price, operations sees flow, maintenance sees risk each in their own system. If those perspectives don\u2019t meet on one screen, decisions arrive late.<\/p>\n

            2) Why is forecast error expensive?<\/h3>\n

            You set a day-ahead plan; then flow\/efficiency changes on-site; the realized generation drifts off plan. That gap becomes a direct cost\/revenue line. That\u2019s why deep-learning-based price forecasting is actively studied (e.g., hourly MCP\/PTF forecasting in T\u00fcrkiye\u2019s DAM). [10]<\/p>\n

            3) Why can\u2019t operational risk be translated into \u201ctrading language\u201d?<\/h3>\n

            Vibration rises on a turbine, but that signal doesn\u2019t show up in bidding decisions as a clear score. Smart SCADA and knowledge-discovery modules allow operators to see comparative KPIs across longer horizons and distinguish operating regimes more clearly. [11]<\/p>\n

            \n
            Mini case: Catching fault signals early with a digital twin.<\/div>\n
            Some operators go beyond alarm-level monitoring. They model how equipment \u201cshould\u201d behave and detect deviations from normal earlier. In hydro plants, digital twin approaches have been shown to improve pre-failure warning capability by simulating sensor faults\/degradation scenarios. This also helps present maintenance findings to trading teams as a simple \u201crisk score\u201d indicator. [13][14]<\/div>\n<\/div>\n

            4) We have data, but we don\u2019t trust it<\/h3>\n

            Sensor errors, missing records, latency\u2026 Digitalization is not only \u201ccollecting.\u201d It\u2019s data quality + traceability.<\/p>\n

            5) Manual management collapses as you scale to multi-plant portfolios<\/h3>\n

            You can somehow manage a single plant manually. Add two or three plants and you\u2019re suddenly balancing different basin conditions, maintenance cycles, and price exposures. Decisions fragment fast. This is exactly why a digital decision system becomes a scaling requirement.<\/p>\n

            Example scenario \/ mini calculation \/ flow<\/h2>\n

            The example below is not financial advice; it\u2019s a simplified scenario to make the logic tangible.<\/p>\n

            Scenario: \u201cReducing deviation\u201d to turn invisible money visible<\/h3>\n

            A hydro plant has an hourly plan for the day.<\/p>\n

            In some hours, generation falls below plan due to flow\/efficiency.<\/p>\n

            Result: deviation\/imbalance cost rises and the team becomes reactive.<\/p>\n

            Mini KPI set:<\/b><\/p>\n

              \n
            • Hourly Planned Generation (MWh)<\/li>\n
            • Hourly Realized Generation (MWh)<\/li>\n
            • Hourly Deviation (MWh) = Realized \u2212 Planned<\/li>\n
            • Hourly Risk Score (maintenance\/fault signals)<\/li>\n
            • Hourly Market Price \/ Reference (MCP\/PTF, SMP\/SMF, etc.)<\/li>\n<\/ul>\n

              Let\u2019s say over a month:<\/b><\/p>\n

                \n
              • There are 200 \u201ccritical hours\u201d (hours with high price impact)<\/li>\n
              • Average deviation in those hours: -1.5 MWh\/hour<\/li>\n
              • With \u201cdigital alerts + early correction,\u201d one-third of that deviation is reduced: 0.5 MWh\/hour improvement<\/li>\n
              • Use a simplified reference price: 2,000 TL\/MWh<\/li>\n<\/ul>\n

                Approximate impact (purely illustrative):<\/b>
                \n0.5 MWh\/hour \u00d7 200 hours \u00d7 2,000 TL\/MWh = 200,000 TL\/month improvement in \u201cdeviation effect.\u201d<\/p>\n

                The main lesson: Digitalization isn\u2019t always a big revolution. Sometimes it\u2019s a small difference in critical hours yet that small difference becomes a real line in the income statement.<\/p>\n

                Flow: The \u201cmost minimal\u201d version of a decision system (MVP)<\/h3>\n
                  \n
                • EP\u0130A\u015e data is pulled automatically (web services). [5][6]<\/li>\n
                • SCADA generation data flows in real time. [1][3]<\/li>\n
                • Dashboard shows \u201cPlan\u2013Realized\u2013Deviation\u201d instantly. [2]<\/li>\n
                • If a deviation threshold is exceeded, an \u201cadjustment\/action\u201d recommendation is produced.<\/li>\n
                • End-of-day report classifies the reason: \u201cflow, efficiency, fault, or data quality?\u201d [11]<\/li>\n<\/ul>\n

                  Figure-2<\/b><\/p>\n

                  \"Figure-2
                  Figure-2 High-level decision-support flow for Hydrowise: SCADA\/OT, EP\u0130A\u015e market data, and operations records are unified in a single data hub, converted into KPIs, and surfaced on a dashboard. When the deviation threshold is exceeded, the system generates an action suggestion (adjustment\/priority note) and closes the loop with an end-of-day root-cause report.<\/figcaption><\/figure>\n
                  \n
                  Risk box: The \u201cWe built a dashboard, we\u2019re done\u201d trap<\/div>\n
                    \n
                  • If KPIs are only displayed, the team still makes manual decisions and delays remain.<\/li>\n
                  • If KPIs are tied to action (threshold\/alert\/recommendation), digitalization produces real value.<\/li>\n
                  • Security\/integration changes in web services are part of operations; you must plan system maintenance accordingly. [6]<\/li>\n<\/ul>\n<\/div>\n

                    Hydrowise \/ Renewasoft approach<\/h2>\n

                    What we\u2019ve described so far becomes \u201cproductized\u201d under Hydrowise in three main blocks:<\/p>\n

                    1) Single panel (Centralized visibility)<\/h3>\n

                    Renewasoft positions Hydrowise as a platform that consolidates hydro plants\u2019 production\/operations\/marketing processes into a single panel. [1] That means different teams look at the same source of truth.<\/p>\n

                    2) AI + forecasting (Insight layer)<\/h3>\n

                    On its technology page, Renewasoft emphasizes forecasting for day-ahead and intraday planning using models fed by meteorological\/hydrological\/SCADA data, supported by scalable cloud infrastructure. [3] This layer is not about a single-number forecast; it aims to provide enough context for decisions (scenarios, confidence, risk).<\/p>\n

                    3) Reporting & BI (Decision layer)<\/h3>\n

                    The reporting & BI approach aims to strengthen decisions through real-time analysis and trend monitoring by turning SCADA and IoT data into meaningful information. [2] The key point: the dashboard must be a decision workspace, not a passive report.<\/p>\n

                    Example Dashboard\/KPI set (ideal for a demo)<\/h3>\n
                      \n
                    • Market KPIs: hourly MCP\/PTF & SMP\/SMF trend, critical hours, volatility indicator<\/li>\n
                    • Generation KPIs: planned vs realized, forecast error, availability<\/li>\n
                    • Water\/reservoir KPIs: reservoir level, flow trend, water value indicator (site-specific)<\/li>\n
                    • Maintenance\/health KPIs: risk score, alarm count, vibration\/temperature trends<\/li>\n
                    • Decision KPIs: deviation band, action recommendation (alert\/priority), \u201ctop 5 critical hours today\u201d<\/li>\n<\/ul>\n

                      Demo CTA (practical offer)<\/h2>\n

                      If you want, a demo doesn\u2019t have to be a generic slideshow. It can be built around a scenario that feels like your actual operation:<\/p>\n

                        \n
                      • 1 week of sample data (SCADA + EP\u0130A\u015e data)<\/li>\n
                      • A minimal dashboard with 10 KPIs<\/li>\n
                      • Deviation band + critical hours screen<\/li>\n
                      • A one-page \u201cend-of-day decision report\u201d<\/li>\n<\/ul>\n
                        \n\n\n\n\n\n\n\n\n
                        Hour<\/th>\nMCP Trend<\/th>\nPlan (MWh)<\/th>\nActual (MWh)<\/th>\nDeviation<\/th>\n<\/tr>\n<\/thead>\n
                        18:00<\/td>\n\u2191\u2191<\/td>\n12.0<\/td>\n11.2<\/td>\n-0.8<\/td>\n<\/tr>\n
                        19:00<\/td>\n\u2191\u2191\u2191<\/td>\n12.0<\/td>\n10.9<\/td>\n-1.1<\/td>\n<\/tr>\n
                        20:00<\/td>\n\u2191\u2191\u2191<\/td>\n11.5<\/td>\n11.3<\/td>\n-0.2<\/td>\n<\/tr>\n
                        21:00<\/td>\n\u2191\u2191<\/td>\n11.0<\/td>\n10.4<\/td>\n-0.6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

                        Table-1<\/b><\/p>\n

                        Table-1 A sample panel enabling combined tracking of MCP trend, planned vs realized generation, deviation, and risk level during critical hours; used to trigger action recommendations when deviation grows.<\/p>\n

                        If we simplify the architecture: sources (SCADA\/OT, market, maintenance) \u2192 integration (automated pull, time sync) \u2192 processing (KPI, forecast, risk score) \u2192 presentation (dashboard, report, alert). Separating these layers makes the system both scalable and manageable. EP\u0130A\u015e web-service access helps automate the market side of this chain; on the plant side, cleaning SCADA streams and producing standardized KPIs follow the same logic. [6][2]<\/p>\n

                        Frequently asked questions<\/h2>\n
                        \n
                        1) Is digitalization in the energy market just buying software?<\/div>\n
                        No. The real value is in collecting data, validating it, translating it into KPIs, and tying it to action. Software is the vehicle. [8][2]<\/div>\n<\/div>\n
                        \n
                        2) Is access to EP\u0130A\u015e data paid?<\/div>\n
                        In EP\u0130A\u015e FAQ content, statements such as free access to the Transparency Platform and web-service offerings appear (details are specified on the relevant pages). [5]<\/div>\n<\/div>\n
                        \n
                        3) Why pull data through web services?<\/div>\n
                        Because it enables automation. Also, transitions to new web-service versions and security updates can change over time; your system must be resilient to these updates. [5][6]<\/div>\n<\/div>\n
                        \n
                        4) Does \u201cwe built a dashboard\u201d mean we\u2019re digitalized?<\/div>\n
                        No. If the dashboard doesn\u2019t generate decisions, it\u2019s just reporting. Digitalization is KPIs turning into action. [2]<\/div>\n<\/div>\n
                        \n
                        5) Do we need AI?<\/div>\n
                        Not necessarily. But when uncertainty is high (weather\/hydrology\/price), forecasting and anomaly detection can create real value. T\u00fcrkiye DAM price forecasting studies show why. [10]<\/div>\n<\/div>\n
                        \n
                        6) Are decision support systems only for trading teams?<\/div>\n
                        No. Even market behavior detection examples show that decision support strengthens data-driven management; different decision layers can be built for different teams. [12]<\/div>\n<\/div>\n
                        \n
                        7) What is the biggest risk of digitalization?<\/div>\n
                        The \u201cwe have data, but we don\u2019t trust it\u201d problem. Data quality, integration sustainability, and security are the backbone. [8][6]<\/div>\n<\/div>\n
                        \n
                        8) Where should I start?<\/div>\n
                        Best start: a 10-KPI MVP with a critical-hours focus. First visibility, then insight, then action. [2][1]<\/div>\n<\/div>\n

                        Conclusion + CTA<\/h2>\n

                        Digitalization in the energy market isn\u2019t \u201cmore data.\u201d It\u2019s fewer surprises. On the hydro side, surprises show up during critical hours: flow shifts, efficiency drops, risk signals rise, market conditions move\u2026 With fragmented screens, it\u2019s easy to miss the moment.<\/p>\n

                        Data-driven decision systems combine EP\u0130A\u015e transparency data [4][5][6] with the plant\u2019s real-time SCADA world [1][3] and turn it into a decision workspace through BI\/reporting. [2] As a result, teams shift from reactive firefighting to a more proactive rhythm.<\/p>\n

                        Demo call:<\/b>
                        \nIf you want to reduce \u201cwhy did deviation grow?\u201d meetings, see critical hours on one screen, and standardize your decision flow, we can shape the Hydrowise approach around a demo scenario close to your plant\u2019s real data.<\/p>\n<\/div>\n

                        [\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

                        [vc_row][vc_column][vc_column_text css=””] RENEWASOFT | TECHNICAL BLOG Reading Time: ~16-20 min \u00a0|\u00a0 Level: Beginner \/ Intermediate Target Audience: HES operators, SCADA engineers, energy company managers Meta description: What does digitalization in the energy market really mean? 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