Developing and Testing a Next Generation Energy Management System

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Written by Marta Marmiroli

An energy management system (EMS) is an essential function needed to increase energy efficiency and to optimally coordinate several energy sources. A large number of systems have been proposed, but not all have the same objectives or technologies. After a classification and definition of EMS, several systems were integrated and tested to understand possible applications and their advantages.

The increased use of renewable energy and distributed resources, along with the need to increase energy efficiency and energy savings, are the main drivers for the implementation and utilization of energy management systems (EMS).

The concept of an energy management system is not new in a power system, but it was commonly believed to be a computer system (a combination of hardware and software) that monitors and controls the high voltage power grid, including transmission and generation. It was controlled by a SCADA (supervisory control and data acquisition) system and a certain number of advanced applications including forecasting and optimization.

The central EMS concept is still very important with a smart grid, but next generation applications are necessary to answer the needs of the changing power system.

EMS is also applied to several levels of energy management: starting at a microgrid or community grid, going down to a unit as small as an independent house. CEMS (Community Energy Management System), FEMS (Factory Energy Management System), BEMS (Building Energy Management System) and HEMS (Home Energy Management System) are commonly used in the smart grid arena to indicate applications that monitor and control resources or load in defined entities (for example, a community, factory, building or house). The main objectives of these applications are normally recognized as consumption optimization and automation of operation.

The differences in objectives imply various technologies and implementations. Illustrated below is a focus on the next generation central EMS. From a technological point of view we individuated three main concepts that are not commonly implemented in a classic central EMS.

  1. The future power system grid is expected to integrate distributed renewables sources. Due to the intermittency of the sources themselves, a stochastic approach to all the optimization application (unit commitment, generation dispatch and so on) is fundamental. For this reason, EMS applications should not optimize the resources operation based on deterministic condition (a fixed load or a fixed forecasted production from renewable generators), but should find a solution that optimizing costs, minimize the risk of a loss of load or over/under frequency operations under several conditions based on the stochastic probability of each condition.
  2. The other peculiarity of the new sources is the distribution on the territory and the size of each source. Compared with a classical power system with large power plants that supply electricity to the network, we are faced with a large number of small sources that have little importance individually, but become massive in total. To overcome this problem, we propose a hierarchical recursive monitoring and control system. Each resource (battery, distributed generator or load) belongs to a group controlled by a SCADA system; the data collected for each SCADA are aggregated and posted to the upper level SCADA as a virtual resource. The upper level SCADA can monitor other real resources such as medium size batteries or generators together with the virtual resources and report to another SCADA monitoring larger resources.
  3. The surplus of energy production from renewable energy in certain hours of the day or times of the year forces the utilization of a storage system to optimize the supply plan. Storage systems may also be utilized for short-term balancing and fluctuation absorption. The next generation EMS has to be properly designed to be able to manage a storage system in addition to conventional generation.

Based on these concepts, a central EMS was developed and tested in a test facility created in a factory in Amagasaki, Hyogo Prefecture, Japan, in 2010. The smart grid test facility is an independent grid with a large number of photovoltaic systems (more than 140) interconnected to a distribution network. Storage systems, including batteries and hydro pump storage, thermal generators and load are also connected to the same grid to recreate an environment similar to the anticipated Japanese grid in 2020. The distribution network is connected to the main power grid with back-to-back converter, (a force-commutated rectifier and a force-commutated inverter connected with a common dc-link) controlled by a digital simulator.

The advantage of having a digital simulator and a back to back is related to the possibility of simulating any kind of network size and configuration allowing the real behavior of the physical equipment to be fed back to the digital simulator. The total system can be considered as a hybrid analog-digital simulator.

The results for the tests can be summarized in three main achievements. The introduction of a stochastic approach to the unit commitment and the economic load dispatch allows the software to produce a plan and a dispatch that is robust compared with a classical deterministic approach, with a minimum increase in fuel costs.

The implementation of a hierarchical recursive SCADA system allows the reduction in the number of high-speed communications interconnections. A local SCADA monitors distributed equipment, and data are estimated and aggregated before being transmitted to the upper layer of the system.

Energy storage systems, if properly operated, have a very high value in the smart grid. Simulation and test results showed that in the forecasted east Japan power system, 2 GW of batteries can help to integrate up to 15GW of solar photovoltaic generation.

In addition to the main functionalities described, integration and testing for demand response functions recently started. Demand response can be an effective tool to reduce production cost and optimize the energy efficiency of the smart grid. Nevertheless a strong and reliable mechanism has to be implemented by governments and utilities before it can become a useful source in the next generation power system.