Energetica India Magazine December 2021

The level of integration and complexity that would be achieved during the energy tran - sition will introduce an increasing complex - ity in the management of these mixed ener - gy systems. Digitalization will play a major part in accelerating efficiency gains. It will drive action by providing data transparen- cy across portfolios, with realtime, model based automated energy management en - suring the systematic achievement of max - imum potential. With the objective to make this complex set of decisions feasible, we believe that any Energy Management System (EMS) sup - porting tool should include and provide the functionalities described below and pre - sented in Figure 3. • Provide integrated, holistic models, that considers not only equipment or subsys - tems usually encountered in conventional energy systems but also what relates to renewable energy sources, such as Photo - voltaic (PV), wind, biomass, hydrogen, etc. • Support forecasting, which estimates future operational site conditions and envi - ronment, like weather, power/fuels market conditions, process energy demand, etc. • Allow the analysis and monitoring of cur - rent and past energy efficiency and perfor - mance of the site and renewable sources. • Support the optimal energy scheduling, taking into consideration availability, fore - casted consumption, variability in electricity prices and inventories, as well as multi-pe - riod related decisions. • Automate the integration of the optimal schedule and real time recommendations to relieve schedulers and operators from com - plex and time-consuming decision-making activities. • Target autonomous operation in the short and medium term, allowing a smooth information flow between the different deci - sion levels, going from planning to the reg - ulatory control layer. Real-Time Optimization Real time optimization was used elsewhere to manage energy systems at the minimum cost, while continuously reducing emis - sions. Two examples are presented: Middle East Refinery: In Ref 3, the results obtained with an MES applied at KNPC Mina Al Ahmadi (MAA) refinery, Kuwait is discussed. The energy system modelled and optimized comprises steam, fuels, electric system, emissions, boiler feed wa - ter, condensates, demineralized water, fur - nace efficiencies, sea water, cooling water, heat exchangers UA, heat loss to product coolers, hydrogen and flaring. Even at a region where energy costs are low, the project challenges were to reduce the total emissions and cost of the energy by automatically generating operational recommendations to help engineers and plant operators in the decision-making process. In addition, energy related KPIs were calculated in real time and historized. In Figure 6, a trend of the economic poten - tial savings (bars) is superimposed with the CO 2 emissions reduction potential. The graphs shows that both objectives were fully met, since savings of 4.4 MM$/y were obtained (because of the potential savings being consistently captured), as well as CO 2 emission reduction of 27,600 tons per year was achieved, European Refinery: In Ref 4, an MES im - plemented at TOTAL Antwerp refinery was presented. The site-wide energy system is monitored and optimized in real time. The EMS was built to be a “tool” for operations to produce electricity and steam at lowest cost, reduce steam losses and follow-up energy cost and performance KPIs on an hourly basis. Energy cost savings of 1.5 to 3.5% (in the order of 1.8 to 2.8 MM€/y) and steam losses reduction of 20-30 t/h were achieved due to real time optimization, as shown in Figure 5. EMS was able to obtain an important over - all site efficiency improvement and, since hydrocarbon-based fuel was used (Natural Gas), GHG emissions were also reduced. This is a clear example of an EMS solution that simultaneously optimized, cost, and emissions, as discussed before. RENEWABLE POWER 42 energetica INDIA- December_2021 Multi-Period Optimization Real time optimization and scheduling, working together and properly aligned, is of paramount importance to operate even better the energy systems at the minimum cost, in particular for those involving renew - ables. The inherent variability of the renew - able energy sources and electricity market prices, along with the need to coordinate energy storage, conventional production backup and other time dependent con - straints, makes optimal energy scheduling a key and pivotal need for tools that aim at managing these energy systems. The multi-period optimization (MPO) tech - nology accounts for restrictions that affect multiple time periods, usually in the future. It is indispensable for taking decisions when