Energy Management Model for Energy District Prosumers and Utility: A Case Study of Texas State I. Hussain Electrical Engineering COMSATS Institute of IT Abbottabad, Pakistan iqrarafridi2000@gmail.com Z. Ullah Electrical Engineering COMSATS Institute of IT Abbottabad, Pakistan engrzahidullah92@gmail.com S. M. Ali Electrical Engineering COMSATS Institute of IT Abbottabad, Pakistan hallianali@ciit.net.pk B. Khan Electrical Engineering COMSATS Institute of IT Abbottabad, Pakistan bilalkhan@ciit.net.pk M. F. Qureshi Electrical Engineering COMSATS Institute of IT Abbottabad, Pakistan fahadq14@gmail.com C. A. Mehmood Electrical Engineering COMSATS Institute of IT Abbottabad, Pakistan chaudhry@ciit.net.pk Abstract— In recent, Smart Grid is heading towards mega grid with the integration of various consumer categories, renewable energy penetration, bi-directional communication infrastructure, and thousands of power buses interconnected in wide area systems. With this growing trend, new terminologies evolve, such as ‘Prosumers in energy district’ that refers to a special class of consumers residing in the vicinity with the capability of producing and consuming energy. Prosumers enhance the smart grid infrastructure in terms of grid-support, bi-directional energy flow, and stability enhancement. With Energy District (ED) as a generating zone, the utility must undergo a Service Level Agreement (SLA) that mutually satisfies the financial benefits. Considering above, we propose a bi-directional energy management model for an ED and Utility. Moreover, a Coalition Manager (CM) is introduced that statistically correlates the weather parameters with electrical load consumption of consumers and controls the energy flows between both parties under the principle of Service SLA. Furthermore, with climatic drifts, an optimization model is developed that maximizes the revenue of the utility. Under SLA umbrella, on Prosumers end, the cost of energy consumption ($/MWh) is minimized and energy surplus is maximized. To validate the aforementioned model, real- time data of Texas State (US) is modeled and analyzed. Keywords—demand response, energy district, energy management, smart grid I. INTRODUCTION A Smart Grid (SG) is the near-future power framework set to achieve demand of electricity in a dependable, feasible, and cost-effective way, employing advanced communication technologies and advanced digital information. The focal motive is to achieve energy sustainability, prevention of large- scale failures, steady availability of power with reduction of Capital Expenses (CAPEX) future for thermal transmission networks and generators and optimized the Operational Expenses (OPEX) of power distribution and production [1]. SG is “the Future Grid” with an advanced, robust, and intelligent system that maintains bi-direction Quality-of-Service (QoS) between utility and consumers. In upcoming stations, end-users must be active rather than desirable or static, and therefore the power flow in SG is capable of dynamically switching between consumers and local renewable energy suppliers [2]. In conventional power systems, drifts in demand and supply will cause: (a) severe voltage dips, (b) imbalance circulating currents, (c) problems of reactive and active power flow, (d) tripping of the load breakers, and (e) degradation in the quality- of-service. The power grid requires energy support to maintain an optimum value of all electrical parameters at various buses across the network. Failure to accomplish energy support will result in massive blackouts affecting millions of consumers. Moreover, with increased downtime will further affect the life and reliability of the power system equipment drastically. With advancement in an SG architecture, monitoring, and control, EM of consumers and utility is enhanced. In [3], the authors proposed a technique of Demand Side Management (DSM) that estimate DSM in Electric Distribution System (EDS), based on Time of Use (ToU) and elasticity pricing. The authors in [4] presented a detailed study of the DR programs and DSM with respect to power industry of China that included China energy policy, Geographical Location (GL) of China, Economic status, microgrid and SG in China, and power industry reform of China. Furthermore, they developed electricity prices and status of DSM in China providing a detailed analysis of DSM of different countries. One side of the research focuses on energy consumption and demand management of residential consumers. The modeling of DR program for consumers to optimizes energy usage was explained in [9]- [12]. In [9], the authors described various aspects of DR and SG that are recently employed, such as (a) control protocols and communication for SG, (b) Architectural models of DR, and (c) technology infrastructure for SG. The authors comprehensively overviewed various DR techniques and programs that motivated consumers to participate in DR programs. Furthermore, different optimal controls are employed for various optimization models for the DR schemes [10]. The authors in [11] discussed in detail the four main aspects of DR, such as (a) DR issues, (b) DR programs, (c) DR 282 2017 International Conference on Frontiers of Information Technology 0-7695-6347-3/17/$31.00 ©2017 IEEE DOI 10.1109/FIT.2017.00057