The integration of distributed energy resources into electric power systems

Small-scale, residential, and distributed energy resources (DER), which are electric vehicles (EVs), heat pumps (HPs), and photovoltaic (PV) arrays, were studied to evaluate their impact on the UK future residential demand and their impact on UK distribution networks. Centralized and decentralized controllers were planned in order to defer reinforcement, while connecting DER units to distribution networks. The centralized controller allocates EV charging durations considering network constraints. The decentralized controller adjusts EV and HP loads based on consumer satisfaction, network constraints, and electricity prices. Normal probability distribution and median filter were used to predict aggregated power of EVs, HPs, and PV arrays on a half-hourly basis over a year. Because of an expected surplus of PV power generation, a considerable demand reduction followed by a sharp demand increase will occur with these residential DER units during summer days in 2035. A low voltage section of test network was used to study the impact of uncontrolled EV charging loads on a three-phase four-wire system. Different combinations of EVs, HPs, and PV arrays were used to investigate their uncertainties in a low voltage section of real network. Real-world trials were used to generate the individual power of residential customers and DER units. Results of unbalanced power flow indicated that network constraints exceeded their limits with a high number of these low carbon technologies. Using an extended section of the test network, the central controller maintains voltage magnitudes, voltage unbalance factors, and power flows within their limits, by re-allocating EV charging durations accordingly. The decentralized controller was designed to minimize electricity bills of EV and HP users. This controller adjusts EV and HP loads to maintain consumer satisfaction and network constraints within their specified boundaries. Consumer satisfaction was determined using mathematical models of EV battery state-of-charge levels and the indoor temperatures of HP houses. The decentralized controller was used to connect predicted numbers of EVs and HPs to a real distribution network, while overcoming the need for network reinforcement, third parties (aggregators), and extensive communication systems