Demand-Side Resources in Active Distribution Systems

Demand-Side Resources in Active Distribution Systems: History

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Subjects: Engineering, Electrical & Electronic

Contributor:

Mukovhe Ratshitanga

Efe Franics Orumwense

Senthil Krishnamurthy

Moteane Melamu

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in active distribution systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of more active distribution networks and that the Internet of Energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS), which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing.

active distribution systems (ADS)
smart metering
control and automation in ADS
optimization techniques in ADS

1. Introduction

The involvement and incorporation of active components such as distributed generation (DGs) have transformed power systems from being passive ones (operated based on unidirectional power flow) to being non-passive (based on bidirectional power flow) or active distributed systems (ADS). ADS is a part of a smart grid (SM) whose operational characteristics are dependent on advanced controllers and smart communication infrastructures, including smart meters, to meet users’ needs and utility preferences effectively. ADS is a relatively recent concept that has arisen in the context of the developments that have evolved over the past few decades in the area of electric power supply. It essentially relates to a salient shift in the manner in which electric power distribution systems are planned, designed, and operated [1].

The distribution network is a key component of the electric power supply system, creating the vital link between (large-scale, centralized) electric power generation and high-voltage transmission on the one side and end-users of the power supply on the other. Traditionally, it has fulfilled this role in a largely “passive” manner, having little to no real-time automation and power flow control. Distribution networks have traditionally been designed to passively cater to all possible load scenarios, as well as contingencies, without much reliance on active control. Moreover, with one-way (and largely predictable) power flow, protection coordination and response to abnormal system conditions have also been fairly manageable, with little need for sophisticated protection solutions. This approach to electrical distribution system design and operation has served the intended purpose quite well for a long time. New developments, however, notably the deregulation of the electric power supply industry (in most countries), as well as the growth of distributed generation, have necessitated a reconsideration of the adequacy of the traditional approach, leading to the concept of “active distribution systems”.

Grid-integrated distributed systems [2] in general have in the last few decades gone from being nearly non-existent to becoming a significant (and growing) component of all new generations in most parts of the world [3,4]. In South Africa, the Renewable Energy Independent Power Producer Procurement Program (REIPPPP), launched in 2011, has since resulted in the contracting of nearly 12 GW of renewable energy, of which about 6.1 GW has thus far been integrated into the electric power grid [5]. Through the Integrated Resource Plan (IRP) 2010–2030, very ambitious targets have been set for the contribution of environmentally friendly generation to the meeting of total South African energy needs going forward, especially given the need to honor the carbon emissions reduction commitments made by the South African government [6] and the ongoing challenge of severe electric power supply shortages the country is facing.

In the context of an ever-growing share of distributed (and especially variable renewable) generation in the power system, the efficient grid integration and management of these new forms of generation have been identified as being pivotal to ensuring the continued operation of a stable, efficient and more economical electric grid [7]. This, in turn, raises the need for efficient control and grid integration strategies that enable the active contribution of distributed energy resources (DER) to the realization of the power system’s operational objectives [2,8]. One strategy for the grid integration of DERs that has been widely studied is that of aggregating a variety of DER technologies and operating them in a coordinated manner to combine their synergistic characteristics and simultaneously enhance the exploitation of intermittent renewable generation. Microgrid and Virtual Power Plant (VPP) are two such aggregation approaches that continue to be active areas of extensive research [9].

A microgrid is generally defined as an interconnection of various DERs (intermittent and dispatchable distributed generation, distributed storage, demand response, etc.) and loads, with suitable control and electrical protection functions to enable it to operate either autonomously or in parallel with the electric grid while meeting the operational requirements in either mode. It is usually considered to be connected to the low or medium-voltage distribution network and has the desirable characteristic that it appears to the grid as a single controllable entity, thus considerably enhancing the grid integration of several heterogeneous DERs [10]. A microgrid may be designed to serve a variety of needs, but a primary goal is often the enhancement of (local) electric power supply reliability, with the microgrid’s capability to operate either in parallel with the grid or autonomously [11].

A Virtual Power Plant (VPP) is yet another concept for the aggregation of a variety of DERs, of differing characteristics and technologies, operated collectively based on an advanced ICT infrastructure and an advanced EMS, to realize more benefits from the coordinated utilization of the available resources than would be possible in the case of individual resource operation [12]. While a microgrid is mainly designed to improve the supply reliability and power quality for a local load or a part of the distribution network [4] the development of the VPP concept has mainly been driven by the intention to effectively integrate DERs into various sections of the electricity market [8]. Additionally, the resources making up a microgrid (to a large extent) tend to be situated in one geographical location, whereas a VPP may aggregate resources spanning a wide geographical topography, relying on advanced ICT infrastructure and an efficient EMS to coordinate the operation of the various resources [13]. These characteristics of the VPP provide an opportunity for it to offer various system-wide services necessary for the reliable operation of the electric power supply system. Distributed generation, demand-side management, and demand response are components that have been identified as having significant potential in supporting the realization of the electric power system’s operational objectives [14].

2. Comparison between Active and Passive Networks

There is a massive energy shift from the traditionally centralized power systems in which power flows in one direction to unidirectional power flow, operating electrical networks with smart grids, communication flow, and information exchange [1]. The centralized electrical network has challenges, such as reliability, scalability, and slow responses from the central point [15]. Continuous technological development is pivotal in how electrical distribution networks are evolving. The solutions from these technological advancements provide benefits to entities operating in that space. Nevertheless, the implementation of such technologies in a slow-maturing distribution network undoubtedly results in challenges [16].

In an ideal working operation, the distribution network works as a perfectly balanced three-phase system. However, the increased penetration of DG and power electronic interface devices are but some of the equipment that introduces disturbances that require mitigation for system stability. The electrical network’s primary objective is to dispatch power between bulk power systems and the consumer and to regulate voltage to its nominal values and power quality [17]. The active distribution network is a system that has adequate control to manage a mix of different generators, loads, and energy storage systems [18]. Passive distribution networks are dependent on significant generating capacity to deal with disturbances in load variation to maintain continuous power flow and have a relatively simple control technique [19].

Active distribution systems possess the ability to handle real-time monitoring and control of the electrical network and are capable of employing distributed generators to provide stability on low-voltage networks [20]. Controlling and monitoring of the active distribution network are carried out through centralized systems and distribution management systems. This is done at the primary substation and at medium voltage (MV) feeder to improve grid stability [21,22].

3. Demand-Side Resources in Active Distribution Systems

Demand-side resources are any technology hardware or software utilized by consumers that can deliver grid services, such as energy dispatch from rooftop solar to the grid, capacity to provide stored power from energy storage systems based on load fluctuations, and provide ancillary services for grid stability [25]. This is attained through the deployment of distribution generators (DG) on a medium- to low-voltage level. DG system is a small electrical generation technology that is located closer to the load. There are several such technologies, which include renewable energy sources, such as wind and solar, natural gas-fired fuel gas, biomass, tidal power, geothermal power, and non-renewable generators [26].

These DG can be directly connected to the grid via power electronic devices or power a local load while dispatching access to the grid. DG can also affect the distribution network negatively due to the unidirectional power flow. The challenges with unidirectional power from the rise in DG lead to voltage quality, protection device settings, increments in maintenance, and fault currents that affect a significant portion of the distribution network. DG, however, possesses several benefits, such as a reduction in transmission and distribution loss due to proximity to the load, which could further assist in alternative expansion. In peak hours, they cater for a portion of the load, therefore, reducing the peak demand and improving power quality and network stability because of their quick start time and ability to dispatch almost instantaneously. In addition, that can also mitigate possible blackouts of the entire network [27]. Demand-side resources can be used in several ways in the energy markets. DR is an active source of active distribution networks (ADN) and also comprises flexible loads, electric vehicles (EVs), DR inflexible loads, and energy storage on the consumer side [28]. Load balancing still remains a challenge in DG dominated by renewable energy sources in which there is a frequent need to match between supply. There are several demand-side management (DSM) resources to mitigate load-balancing challenges and avoid the need to use the supply-side resources to balance renewable power increase. The demand-side resources can be classified into energy efficiency and demand responses. Electrical power networks have embarked on programs that encourage the use of high energy efficient appliances, such as lighting or equipment that employs variable speed drives. This, however, does not form an active resource, which means it cannot balance renewable fluctuations in real time, but energy efficiency, therefore, balances renewable fluctuations over a longer period through resource planning [29].

4. Communication Technologies Implementation for Control and Automation Capabilities in Active Distribution Networks

The main foundation for the management of ADNs is the ability of several entities, such as operators of DGs, aggregators, distributed system operators, and transmission system operators, to interact with each other for delivering monitoring, control, and real-time exchange of energy consumption and power usage data. Also, the transmission system operators and distributed system operators can retrieve consumer usage data and online pricing and optimize the electricity distribution based on the electricity consumption via a communication network. Thus, a fast, reliable, and secure communication infrastructure plays a fundamental role in the management of ADNs. With the adoption of information and communication technologies (ICT) in the management of ADNs, it is possible to enhance the efficiency of power generation, transmission, and distribution. ICT aids in the accumulation of information from every point of ADNs and can be used for demand forecasting, network planning, ADNs operation, control and protection of ADNs, and optimizing the performance of ADNs [51].

ICT also assists in managing the complexity of ADNs effectively and the implementation of control and automation functionalities [52]. The heart of modern ADNs lies in their ability to communicate, collect data, and make real-time decisions. This work reviews the critical role played by communication technologies, such as the Internet of Things (IoT), 5G networks, and Low-Power Wide-Area Networks (LPWANs). It discusses their suitability for different use cases within ADNs, including monitoring, control, and demand response. The distance coverage, rates, ranges, and power consumption of several wireless communication technologies that can be employed in ADNs are depicted in Figure 1. A key requirement for the development of the IoT is achieving low power consumption. There are also additional requirements that need to be considered in addition to reducing power consumption in ADNs [53].

Figure 1. Data rate, coverage distance, and power consumption for various protocols [53].

5. Energy Storage for Demand-Side Resources

Energy storage plays a crucial role in optimizing demand-side resources. Demand-side resources play a vital role in the energy sector as they offer important benefits for both consumers and grid operators. These resources refer to the ability of consumers to actively manage their energy consumption patterns and contribute to grid stability. By incorporating energy storage technologies, demand-side resources can become even more valuable [88]. Storage allows consumers to shift their energy usage to times when electricity is cheaper or more readily available, reducing costs and optimizing energy efficiency. Additionally, energy storage enables consumers to store excess electricity generated from renewable sources and use it during high-demand periods, thereby enhancing grid reliability and reducing dependence on fossil fuels. This integration of energy storage with demand-side resources presents numerous opportunities for a more sustainable and resilient energy system in the future. In a study by [89], the authors proposed a framework where neighboring microgrids form a multi-microgrid (multi-MG) to install a shared Cloud Energy Storage (CES) with the aim of increasing profit and improving reliability. They went further to evaluate different investment scenarios, considering the yearly reward from Transmission System Operators (TSO) and Distribution System Operators (DSO), and provided a decision table for selecting the best investment scenario. This work contributes to providing a practical plan to make CES investment affordable for microgrids and considering multiple criteria in the decision-making process.

The authors in [90] presented a stochastic model and a novel mixed-integer model to determine the optimal capacity and operation of Large-scale Energy Storage Systems (LESS) in coordination with a demand response scheme. The proposed model considers managerial options, such as the number of allowed demand response actions and the number of allowed charging and discharging. From the work, the simulation results demonstrated the significant economic impacts of considering these options. This work aimed at enhancing the economic viability of demand response programs by integrating large-scale energy storage systems into the distribution substation.

Community Energy Storage (CES) is an effective tool for congestion management and operational cost optimization in distribution systems, thus, providing economic benefits to both the microgrids and the (DSO). The significance of CES in tackling congestion- and cost-related concerns in distribution systems is emphasized in [91], and the demonstration of the effectiveness of CES in lowering energy prices for the microgrids and controlling congestion in the distribution system is studied. The work employed a Teaching-Learning-Based Optimization (TLBO) algorithm to optimize the operation of the battery within microgrids, resulting in a decrease in energy costs for the microgrids. Additionally, the CES system is utilized to alleviate congestion in the distribution system, with the DSO purchasing charged power from the storage manager, even at a premium price exceeding the prevailing electricity rate during congested hours. This mechanism effectively alleviates congestion and provides economic benefits to both the microgrids and the DSO. Furthermore, the authors also presented a comparative analysis of heuristic algorithms, hence, highlighting the superior performance of the TLBO algorithm in achieving cost reduction and optimizing the distribution system, leading to cost efficiency.

The impact of energy storage facilities on the flexibility of active distribution networks cannot be underestimated, as it plays a vital role in eliminating challenges related to renewable energy resources. In the work by the authors in [92], a stochastic economical flexibility evaluation method based on the envelope of the feasible operation region to the uncertain space was proposed. The work provided insights into the impact of different uncertain sources on the flexibility quantification process while taking into account the distribution network’s operation cost. The flexibility that storage systems provide to the distribution networks is quantified economically, and sensitivity analysis is performed to evaluate how battery storage system capacity impacts the flexibility index.

Energy storage for demand-side resources offers several benefits and presents certain challenges. One of the main advantages is the ability to provide a reliable and stable energy supply during peak demand periods. This helps to ensure grid stability and reduce the need for additional power generation infrastructure. Energy storage also enables demand response programs, allowing consumers to actively manage and optimize their energy usage. Additionally, it facilitates the integration of renewable energy sources by smoothing out fluctuations in supply. However, there are challenges associated with energy storage, such as high upfront costs and limited technological advancements. The challenges related to thermal storage are examined in [93], where a state of the art of present applications of thermal storage for demand-side management are examined. The effectiveness of thermal energy storage for managing the mismatch between the availability of renewable electricity and the demand for electricity in buildings where hot water, heating, and cooling are delivered by heat pumps and air cooling is investigated. Results showed a reduction of 46.7% in the peak demand and a shift of approximately 37% of the total energy consumption from the peak and shoulder period to the off-peak period.

In the future, energy storage for demand-side resources is expected to undergo several trends. Firstly, there will be a shift towards more advanced battery technologies, such as lithium-ion and flow batteries, which offer higher energy densities and longer lifespans. Secondly, there will be a greater emphasis on decentralized storage systems, allowing individual consumers to store and manage their own energy. This will be facilitated through the use of smart grids and advanced control systems. Additionally, the integration of renewable energy sources with energy storage will become more prevalent, enabling a more reliable and sustainable energy supply.

6. State of Art in Control and Automation in Distribution Networks

The control and automation technologies in ADNs are very important. They enhance grid stability, enable real-time decision-making, and optimize energy distribution. Control algorithms have evolved significantly, enabling more precise and adaptive control strategies [94]. The latest developments in control algorithms include model predictive control (MPC), distributed control, and machine learning-based approaches. The distributed control system is an essential component of an ADN, enabling efficient, reliable, and resilient grid operation in the face of increasing complexity and the integration of renewable energy sources [14]. It empowers utilities and operators to manage the dynamic nature of ADNs effectively and adapt to evolving energy landscapes, ultimately contributing to a more sustainable and efficient energy distribution system. Supervisory Control and Data Acquisition (SCADA) systems are the backbone of control and automation in ADNs. The control and automation technologies are indispensable for managing the complexities of ADNs [21].

Active distribution networks (ADNs) enable grid operators to adapt to the changing energy landscape, integrate renewable resources, optimize grid operation, enhance grid resilience, and respond to grid events in real-time, ultimately contributing to a more reliable, efficient, and sustainable energy distribution system. This section critically evaluates the enabling technologies that facilitate control and automation in ADNs, with a primary focus on control and automation technologies themselves. The main operational goal of the distribution network is to deliver electricity of an adequate level of quality securely and reliably while ensuring economical, efficient, and environmentally sustainable operation. To facilitate the realization of this goal, the distribution network operator (DNO) makes use of a distribution management system (DMS). A DMS is essentially a set of functionalities (or applications) that facilitate the monitoring, control, and automation of distribution network operations. It acts as a decision support system, utilizing which the DNO can have a real-time overview of the state of the network and initiate automatic or manual actions meant to maintain reliable and efficient system operation or mitigate anomalous network conditions. Reliability and quality of supply are ensured primarily by minimizing the occurrence of power outages, minimizing the outage time, and maintaining the system frequency and voltage magnitude at nominal levels throughout the network [95].

A distribution management system (DMS) can also be seen as an approach that unites primarily disparate facilities to operate the distribution system in an effective, efficient, and organized way. It has several functionalities to assist operators in managing and optimizing distribution networks through suitable decision-making. A DMS has two types of application functions, which are the (i) real-time application functions and (ii) analytical application functions [96].

DMS is also required to perform a variety of on- and offline analyses to implement such functionalities as fault location, isolation service restoration, and network reconfiguration. A Supervisory Control and Data Acquisition (SCADA) system is an integral component of a DMS, which enables network monitoring, automation, and control. The architecture and key functionalities of DMS are seen in Figure 2. Among the key functionalities of the DMS, the following can be identified, some of which are also depicted below.

Distribution system power flow (DSPF): The voltage phasors are computed on the nodes given the information of the grid characteristics and power dispatched, representation supply, and loads. Real-time DSPF employs forward/backward sweep for radial network and current injections for meshed distribution network [97,98].

Volt/VAR control (VVC): This is a DMS functionality that is mainly employed in managing voltage levels and reactive power flows throughout the network. Reactive power flows have a major impact on the network voltage profile. Devices employed for VVC encompass those that directly inject reactive power into the grid (e.g., shunt capacitors and shunt reactors), and those that directly control voltage (e.g., load tap-changing (LTC) transformers and voltage regulators). Traditionally, LTCs, voltage regulators, and capacitor banks have been the primary devices used in implementing VVC. With the continued gradual increase in the amount of distributed energy resource technologies integrated into the distribution network, however, these are also anticipated to take part in VVC. Smart inverters, for example, can inject reactive power into the grid and thus constitute a good alternative VVC resource [99,100]. The VVC functionality facilitates the mitigation against excessively low or high voltages, which may not only impact the power quality but might additionally pose a threat to the safety of electrical equipment. Besides voltage regulation, VVC also plays an important role in minimizing system losses and relieving key network equipment (such as transformers and feeders) of excessive loading (by reducing the capacity required to satisfy a given load demand).

Load shedding management: This is a DMS functionality that enables controlled balancing of supply and demand, where the demand exceeds the available supply. The main aim is to preserve the integrity of the entire distribution network by interrupting the electricity supply to non-critical loads to prevent a supply shortfall from cascading and threatening to cause the failure of the whole system. An automated load-shedding management application can detect predetermined trigger conditions that necessitate the initiation of predefined load-shedding control actions. If non-automated, operators have the responsibility to manually initiate the load-shedding operations, which should be done in a safe and controlled manner to always maintain normal service to as large a part of the network as possible. Trigger conditions for load shedding, whether automated or manually initiated, include under-frequency load shedding, limit violations, and time-of-day-based load shedding [101].

Load balancing: This involves re-routing loads to other parts of the network to relieve certain distribution equipment of overload conditions. This is often achieved through network reconfiguration. A feeder load management unit in the load balancing functionality monitors the loads on the feeders and identifies possible areas of congestion that may be susceptible to overload conditions. Feeder reconfiguration may also be used for other network operation improvement measures, such as voltage profile improvement and loss reduction. Other benefits of load balancing include improved network utilization and reduced stress on key network infrastructure [101].

Distribution system state estimation (DSSE): It uses the state vector of the distribution network for estimation, where the least square approach is used on redundant measurements [98]. The industrial sector requires premium power quality, so either current or power methods are used to achieve high computational performance [102,103].

Optimal feeder reconfiguration (OFR): The main task of this function is to find ways to reduce generation losses by selecting the best radial system configuration. This is attained by solving an optimization that arranges the structure of the distribution network and has a load balance between feeders. The frequently used method due to the challenges of optimization is the switch exchange heuristic [96,104,105].

Distribution system short circuit computation (DSSCC): This function evaluates the capability of circuit breakers to perform adequately under maximum current operation conditions. It further checks on relay sensitivity under minimal current faults [97,106].

Figure 2. Architecture and key functionalities of a DMS [101].

Fault Locator (FLOC): It employs statuses of fault locators that communicate with DMS to locate fault branches in a network and then employs terminal impedance-based method approaches to precisely localize the faulty line [98,107]. Fault Isolation (FISO): This is a method of isolating a fault by opening circuit breakers. It isolates a small portion of the distribution network by opening switches upstream and downstream of the fault [108]. Service Restoration (SRE): Dispatches power to load nodes affected due to FISO via alternative feeders [105].

Load forecasting: This is a key component of the distribution management system that enables the system operator to assess the short-, medium-, and long-term expected load on the network, which then facilitates network planning and network infrastructure development. Short-term load forecasting is considered to have a duration of up to one day, medium-term load forecasting is considered to have a duration of up to one year, and long-term forecasting is considered to have a longer duration of up to ten years. With the growing integration of distributed generation in the distribution network, generation forecasting is also becoming a key functionality of the DMS. This involves the analysis of weather patterns, seasonality, and other data to estimate the output that can be expected from distributed generation, especially the weather-dependent intermittent renewable generation (e.g., photovoltaic and wind power generation). Effective load forecasting contributes greatly to reliable, economical, and efficient network operation [109].

The transition from passive to active distribution networks necessitates the development of advanced distribution management system functionalities that can handle the growing complexity of distribution network operation in the presence of a variety of active distributed resources, such as distributed generation, distributed energy storage, demand-side management, and demand response.

7. Optimization Techniques for Demand-Side Resources in Active Distribution Networks

The integration of demand-side resources (DSRs) into active distribution networks has gained significant attention in recent years due to the increasing need for more sustainable and efficient energy systems. Thus, it is important to explore the optimization techniques employed to harness the full potential of DSRs within active distribution networks, with a particular focus on the modeling of these resources. The effective utilization of DSRs can enhance grid reliability, reduce energy costs, and promote environmental sustainability. Table 1 outlines the various optimization methods in the literature and their limitations.

Table 1. Optimization methods and limitations.

Authors	Type	Implementation	Resources	Limitations
[131]	MILP (Classical)	Reduced annualized cost by optimally selecting several system components and renewables on a smart grid.	Grid-tied with microgrid with solar PV, CHP, backed-up boilers, and loads (Simulation-based).	Struggles to handle the optimization of multi-input and output systems.
[132]	Multi-objective framework using MILP (Classical)	Avoid power export by optimizing the multiple cogeneration systems, such as combined heat and power in microgrid residential areas. An operational planning model to mimic energy loss characteristics between storage tanks and a hot water calculating model regarding energy loss on network pipes were developed using MILP. This resulted in a reduction in residential units involved in the hot water supply network.	Grid-tied with microgrid residence cogeneration system, gas-fired boiler, storage tank, and loads (hot water demand) (Simulation-based).	Struggles to handle the optimization of multi-input and output systems. It struggles to handle the system with high disturbances.
[133]	Fuzzy logic-based decision-making framework (Heuristic)	Optimize power dispatched to the grid through storage systems. Maximize electricity generation through renewables and revenue to microgrid owners during time-varying electrical costs.	Grid-tied microgrid with renewable energy sources, battery storage, and loads (Simulation-based).	Slow in transient and systems with a high volume of dynamics.
[134]	MILP(Classical)	Investigates how the combination of electrical and thermal storage can reduce energy cost by enabling the microgrid to improve using its power produced in-house.	Grid-tied with microgrid solar PV, geothermal heat pumps, solar thermal energy plant, thermal energy storage, battery storage, and loads (Simulation-based) (Real data).	However, high investment costs made them unprofitable at the current price condition.
[135]	Improved teaching learning-based optimization (Heuristic)	Minimize the impact of intermittency and fluctuation of renewables by controlling DG output power, altering network topologies, and managing demand-side load.	Grid-tied microgrid with wind turbines, solar PV, and loads (Simulation-based).	Inability to predict the future behavior of the system.
[136]	Advanced model prediction control (Heuristic)	Maximize the high penetration of renewables in the microgrid and minimize the running cost by solving optimization problems at each sampling time while meeting the demand and accounting for technical and physical constraints.	Grid-tied microgrid with battery storage, fuel cells, wind turbines, hydrogen electrolyzer, solar PV, hydrogen tanks, and loads (Simulation-based).	Scalability, complexity, and controllability challenge.
[137]	MPC (Heuristic)	Minimize energy cost and maximize battery lifespan by employing a microgrid central controller to optimally choose the adequate pattern for charging and discharging.	Grid-tied microgrid with energy storage, wind, and solar PV (Simulation-based).	Slow in handling fast transient systems.
[138]	Fuzzy logic adaptive prediction control (Heuristic)	Tune the input parameter on a cost function from the diesel generator and fuel cell to optimally regulate frequency in the microgrid.	Standalone microgrid that is made up of fuel cells, diesel generator, wind turbine battery storage, and loads (Simulation-based).	The high number of input variables affects model formulation, leading to more computational power needed.
[139]	Stochastic receding horizon control	Minimize uncertainties from renewable energy sources by employing simplified Z-Bus with sequential linear programming to linearize non-linear system dynamics. The controllable DG, switchable shunt capacitor, storage unit, and on-load tap changing transformer are jointly optimized to reduce cost, and constraint violations are mitigated.	Grid-tied microgrid with solar PV, wind turbines, and loads (Simulation-based).	Slow in handling fast transient systems.
[140]	Enhanced model predictive control (Heuristic)	Minimize consumption from the grid, improve battery lifespan, and increase renewable sources’ participation in catering for the load.	Grid-tied with microgrid PV, Battery ban, and loads (Simulation-based).	Accuracy of models is a challenge.
[141]	Adaptive predictive control (Heuristic)	Minimize frequency fluctuations in the existence of disturbances and mitigate oscillations caused by external disturbances on tie line variation.	Grid-tied microgrid with a diesel generator, flywheel, battery storage, fuel cell, wind turbines, hydrogen electrolyzer, and loads (Simulation-based).	The model becomes complex when handling a large number of controls.

Principles and Different Methods Used in Classical Optimization
Classical optimization methods operate on the assumption that the objective function
and constraints are deterministic and known with certainty. The core principle is to
identify the optimal solution by systematically searching through the feasible solution
space. Key elements of these methods include mathematical modeling, objective function
formulation, decision variables, and constraints. The foundation of classical optimization
methods lies in mathematical modeling. It is crucial to know how optimization problems
related to DSRs are represented mathematically. It represents the formulation of objective
functions, identification of decision variables, and incorporation of constraints. An accurate
mathematical representation is critical for developing optimization models that reflect the
unique characteristics of DSRs.
Linear programming is particularly well-suited for optimizing DSRs in active distribution
networks where objectives and constraints are linear [142]. LP models can be used
to minimize operational costs, such as energy procurement costs or grid infrastructure
investments, while adhering to constraints related to capacity, voltage limits, and DSR
availability. LP provides valuable insights into cost minimization and resource allocation.
The integration of discrete decisions into DSR optimization is addressed by mixed-integer
linear programming [143]. MILP models are beneficial for problems that involve both
continuous and discrete decision variables, which are common in DSR management [144].
Examples include determining optimal load-shedding strategies during demand response
events or selecting the best time to charge electric vehicles (EVs).
Deterministic optimization methods are instrumental in load scheduling for DSRs.
Classical techniques, such as linear programming and mixed-integer programming, are
used to optimize the timing and magnitude of electricity consumption for various DSRs,
including industrial processes, HVAC systems, and residential loads. These methods ensure
efficient energy utilization while meeting operational and economic objectives. Classical
optimization methods are employed in demand response (DR) programs, where the goal
is to optimize DSR participation to balance supply and demand [145]. These models
allow utilities to maximize the economic benefits of DSR participation while ensuring
grid stability.
7.4. Heuristic/Non-Deterministic Optimization Methods
Heuristic and non-deterministic optimization methods have gained prominence in
optimizing demand-side resources (DSRs) within active distribution networks. These methods
offer valuable tools for solving complex, non-linear, and computationally intensive
DSR optimization problems [146]. Heuristic and non-deterministic optimization methods
are characterized by their ability to handle complex and computationally challenging
problems. Unlike classical optimization methods, they do not guarantee globally optimal
solutions but provide near-optimal solutions within a reasonable computation time.
These methods are particularly well-suited for DSR optimization in dynamic and uncertain
environments [147].
Metaheuristic algorithms, such as genetic algorithms and particle swarm optimization,
are widely used in DSR optimization [148]. Genetic algorithms mimic the process of natural
selection to evolve potential solutions to optimization problems [149]. They are effective in
optimizing DSRs by exploring a diverse solution space and iteratively improving solutions.
Genetic Algorithms (GAS) have been applied to DSR management problems, such as
load scheduling and demand response planning, often yielding near-optimal solutions in
complex scenarios. Particle Swarm Optimization (PSO) is inspired by the social behavior
of birds flocking or fish schooling. It optimizes DSRs by iteratively adjusting potential
solutions based on both individual and group performance. PSO has been applied in DSR
optimization to tackle problems involving distributed resources, such as distributed energy
generation and load control [150].

Conclusions
This paper provides an overview of control, automation, and optimization of demandside
resources in active distribution systems. The literature survey is based on several
developments in the distribution network, which is a linchpin of electric distribution power
supply. The fundamental differences between passive and active distribution networks
were investigated, and their differences are presented with and providing a unidirectional
flow of power, which is beneficial to both the utility and the consumer in terms of financial
benefits and provides more energy stability and grid resistance. The planning and design
of the traditional grid were looked at, and the role of DNO, network security, and reliability
with performance indicators such as LOP, LOLE, reserve margin, and EENS. The study also
investigated the planning and design of ADN, which is influenced by energy markets and
technology innovation.
The innovation in the information technological space, growth of DG, and deregulation
of the electric power supply industry fast-tracked the concept of active distribution
systems. The volume of information exchange between different players in the active
distribution network requires adequate protocol to standardize data models in the electricity
system equipment and provide operational specifications, such as interoperability,
which is why the IEC 61850 standard is implemented. The high information exchange
results in vulnerabilities and attacks through attaining access to substation automation
controls, such as GOOSE flooding and intrusion of SMV, which poses a major challenge in
communication and data exchange. Moreover, this also affects smart metering technologies
through the generation of the interfering signal and high frequencies on the communication
line, but different mitigation measures are taken, such as integrated anomaly detection and
data collected from simulated attacks, employing software-defined network (SDN) switch
eliminates possible paths of intrusion and network data overload and intrusion detection
system as a countermeasure.
The optimization techniques of demand-side resources in and, such as the deterministic
optimization methods and non-deterministic methods, were critically studied. The work
concludes that IoE is becoming a major player in ADS, which assists in the reduction of
faults due to human error, fast responses, and improved stability of power supply. It is also
worth noting that automatization and control also have financial benefits to the consumer
due to the bidirectional flow of power. In DRs, at a low-voltage level, the challenge becomes
the privacy of their information, how to claim compensation from the markets is still not
vivid enough, and the accuracy of the measurements from the consumer to the ISO. Cyber
threats and security breaches are also and will still be a continuous challenge that needs
frequent evaluation and mitigation measures so as not to collapse the power supply system.
Multi-input and output systems are a major challenge to both the classical optimization
techniques and the heuristic ones. ADN is made up of complex non-linear systems whose
requirements need techniques that respond timely, effectively, and adequately to different
configurations. Classical optimization, however, cannot handle such complexities. In
heuristic techniques, the high frequency of change in system dynamics affects the model
formation of MPC due to the complexities and requires high computational power to run
such simulations. The introduction of artificial intelligence (AI) could further improve the
performance of techniques, such as MPC in microgrids, and deep learning could also deal
with imprecise models.

This entry is adapted from the peer-reviewed paper 10.3390/app132312573

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